PROJECT DELIVERABLE REPORT Project Title: Zero-defect ... · “This project has received funding...

“This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723906”

PROJECT DELIVERABLE REPORT

Project Title:

Zero-defect manufacturing strategies towards on-line

production management for European FACTORies FOF-03-2016 - Zero-defect strategies at system level for multi-stage manufacturing in

production lines

Deliverable number D9.4

Deliverable title Data Management Plan (DMP)

Submission month of deliverable M6

Issuing partner 7- DATAPIXEL

Contributing partners All partners

Dissemination Level (PU/PP/RE/CO): PU

Project coordinator Dr Dionysis Bochtis

Tel: +302421096740

Email: [email protected]

Project web site address http://www.z-fact0r.eu/

Document Information

Ref. Ares(2017)2503178 - 17/05/2017

http://www.z-fact0r.eu/

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Filename(s) Z-Fact0r_D9.4

Owner Z-Fact0r Consortium

Distribution/Access Z-Fact0r Consortium

Quality check EPFL, ATLANTIS, CETRI, CERTH, DATAPIXEL

Report Status Final

Revision History

Version Date Responsible Description/Remarks/Reason for changes

1.0 10.03.17 DATAPIXEL Report draft write-up

1.1 15.03.17 ALL PARTNERS Inclusion of partners’ contributions

1.2 21.03.17 DATAPIXEL Second revised draft

1.3 27.03.17 DATAPIXEL Inclusion of ATLANTIS contribution

1.4 31.03.17 ALL PARTNERS Inclusion of partner´s contribution 2

1.5 31.03.17 DATAPIXEL Internal Review

1.6 13.04.17 CETRI Contribution of the Dissemination and Exploitation Manager and WP9 leader

1.7 24.04.17 DATAPIXEL Third revised draft

1.8 03.05.17 DATAPIXEL Inclusion of final contribution of the partner´s

1.9 09.05.17 DATAPIXEL Final version for peer-review process

1.91 10.05.17 EPFL Peer Review

1.99 11.05.17 ATLANTIS,

CERTH Final version for technical manager & coordinator review

2.0 16.05.17 DATAPIXEL Final Review ready for submission to the EC

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Contents

1 Summary ............................................................................................................................................................... 5

2 Introduction ......................................................................................................................................................... 5

2.1.1 Participation in the pilot on open research data .............................................................................. 6

2.1.2 Building a DMP in the context of H2020 ......................................................................................... 6

2.2 Z-Fact0r Data Management Plan (DMP) .............................................................................................. 7

2.2.1 General description ............................................................................................................................... 7

2.2.2 Activities of Data Management Plan ................................................................................................. 8

2.2.3 Register on numerical datasets generated or collected in Z-Fact0r .............................................. 8

2.2.4 Metadata for Data Management ......................................................................................................... 9

2.2.5 Data description .................................................................................................................................. 10

2.2.6 Policies for access, sharing and re-use ............................................................................................. 62

2.3 Data currently being produced in Z-Fact0r ........................................................................................ 64

3 Data Management related to Zero-defects Manufacturing ........................................................................ 65

4 Data Management Portal (FREEDCAMP) .................................................................................................. 65

4.1 FREEDCAMP portal functionalities ................................................................................................... 65

4.2 Data Backup: Private Area of the Project Website ............................................................................ 68

4.3 Open Access Section .............................................................................................................................. 68

5 Future Work ...................................................................................................................................................... 68

5.1 Roadmap of actions to update the DMP ............................................................................................. 69

6 Conclusions........................................................................................................................................................ 70

7 Glossary .............................................................................................................................................................. 70

8 Bibliography ....................................................................................................................................................... 71

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Abbreviations

DMP Data Management Plan

D9.4 Deliverable 9.4

ORD Pilot Open Research Data Pilot

OA Open Access

EU European Union

EC European Commission

CCS Consortium and the Commission Services

H2020 Horizon 2020

R&D Research and Dissemination

ODF Open Document Format

ODT Open Document Text

WP Work Package

GA Document of Grant Agreement

CA Document of Consortium Agreement

IPR Intellectual Property Rights

M6 Month 6

DEM Dissemination and Exploitation Manager

KPI Key Performance Indicator

RCA Root Cause Analysis

i-Like Intelligent Lifecycle Data and Knowledge

DSS Decision Support System

ES-DSS Early Stage-Decision Support System

KMDSS Knowledge Management and Decision Support System

GD&T Geometrical Dimensions and Tolerances

MP Measurement Plan

CERIF Common European Research Information Format

PLC Programmable Logic Controllers

DCE Dissemination, Communication and Exploitation

RDI Research Data Information

QIF Quality Information Framework

DB Database

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1 Summary

This report focuses on the preparation of the DMP for Z-Fact0r project. DMP provides an analysis

of the main elements of data management policy that will be used throughout the project with regard

to all datasets that will be generated. In particular, DMP will define how this data will be managed

and shared by the project partners, and also, how this information will be curated during as well as

preserved after the project duration.

DMP of Z-Fact0r project describes the life cycle of all modelling and observation data collected and

processed during the project, giving an overview of available research data, access and data

management as well as terms of use. The DMP reflects the current state of the discussions, plans

and ambitions of the partners, and it will be updated and augmented with new datasets and results

during the lifespan of Z-Fact0r project.

The ORD Pilot of the EC aims to improve and maximize access and reuse of research data generated

by projects focusing on encouraging good data management as an essential element of research best

practice. Following the recommendation of the EC, Z-Fact0r project is participating in the ORD

Pilot and DMP is included as D9.4 deliverable (M6) of WP9 DCE that has been prepared during

the first 6 months of the project.

2 Introduction

The amount of data generated is continuously increasing while use and re-use of data to derive new

scientific findings is relatively stable. This information would be useful in the future if the data is

well documented according to accepted and trusted standards which enable the recognition of

suitable data by negotiated agreements on standards, quality level and sharing practices. For this

purpose, DMP defines strategies to preserve and store data over the defined period of time in order

to ensure their availability and re-usability after the end of Z-Fact0r project.

According to the Guidelines of ORD Pilot in H2020, research data refers to information, in

particular facts or numbers, collected to be examined and considered and as well as basis for

reasoning, discussion, or calculation. The overall objective of Z-Fact0r project is to develop zero-

defect manufacturing strategies for on-line production. Z-Fact0r aims to contribute to the

eradication of defects in manufacturing, providing better quality of products, increasing flexibility,

and reducing production costs. Thus, research activities are more focused on the production process

and tools than on production of research or observation of data, so the amount of research data

which will be produced within the project is limited, at least at this stage of the project.

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2.1.1 Participation in the pilot on open research data

The EC is running a flexible pilot under H2020 called the ORD pilot. The ORD pilot aims to

improve and maximize access and re-use of research data generated by H2020 projects and takes

into account the need to balance openness and protection of scientific information,

commercialization and IPR, privacy concerns, security as well as data management and preservation

issues. The 2017 work programme of ORD pilot has been extended to cover all the thematic areas

of Horizon 2020.

Following the recommendation of the EC, Z-Fact0r project is participating in the ORD Pilot and

DMP is D9.4 deliverable (D.9.4) due M6 of the project. The DMP of Z-Fact0r project has been

prepared by taking into account the document template of the “Guidelines on DMP in H2020”.

This document will be updated and augmented with new datasets and results, according to the

progress of the activities of the Z-Fact0r project. Also, the DMP will be updated to include possible

changes in the consortium composition and policies over the course of the project.

The procedures that will be implemented for data collection, storage, access, sharing policies,

protection, retention and destruction will be according to the requirements of the national legislation

of each partner and in line with the EU standards.

2.1.2 Building a DMP in the context of H2020

The EC provided a document with guidelines for project participating in the pilot. The guidelines

address aspects like research data quality, sharing and security. Following these guidelines, DMP will

be developed with aim to provide a consolidated plan for Z-Fact0r partners in the data management

plan policy that the project will follow.

The consortium will comply with the requirements of Directive 95/46/EC of the European

Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to

the processing of personal data and on the free movement of such data. The consortium will

preserve the right to privacy and confidentiality of data of the survey participants, by providing them

two documents: The Participant Information Sheet and the Consent Form. These documents will

be sent electronically and will provide information about how the answers will be used and what the

purpose of the survey is.

The participants will be assured that their answers will be used only for the purposes of the specific

survey. The voluntary character of participation will be stated explicitly in the Consent Form. Before

conducting the survey, the consortium will examine and follow the requirements of the national

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legislation in line with the EU standards, whether the proposed data collection requires special

local/national ethical/legal permission.

An ethical approach will be adopted and maintained throughout the fieldwork process. The

responsible partners will assure that the EU standards regarding ethics and Data Management are

fulfilled. Each partner will proceed with the survey according to the provisions of the national

legislation that are adjusted in line with the respective EU Directives for Data Management and

ethics.

The consortium will follow a transparent recruitment process for the engagement of stakeholders

and inclusion/exclusion criteria for all the surveys will be explained in the Participant Information

Sheet.

Each partner will send an invitation (by mail) to participants/third parties that have neither the role

in Z-Fact0r project nor professional relationship with the consortium to participate in the survey.

The consortium will also examine whether personal data will be collected and how to secure the

confidentiality in such a case.

The Steering Committee of the project will also ensure that EU standards are followed. The issue

of informed consent for all survey procedures, all participants will be provided with a Participant

Information Sheet and Consent Form to provide informed consent. The default position for all data

relating to residents and staff will be anonymous.

2.2 Z-Fact0r Data Management Plan (DMP)

2.2.1 General description

This document outlines the first version of the project’s DMP. The DMP is presented as D9.4 public

deliverable (Month 6) of WP9, DCE.

The main purpose of DMP is to provide an analysis of the main elements of data management policy

that will be used by the consortium with regard to all the datasets that will be generated by the project

(e.g. numerical, images, etc.).

This document describes the Research Data with the metadata attached, and presents an overview

of datasets to be produced by the project, their characteristics and the management processes to

make them discoverable, accessible, assessable, usable beyond the original purpose, and

disseminated between researchers. It also introduces the specifications of the dedicated Data

Management Portal developed by the project in the context of the ORD Pilot, allowing the efficient

management of the project’s datasets and providing proper OA on them for further analysis and

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reuse. In addition, the DMP of Z-Fact0r project reflects the current status of discussion within the

consortium about the data that will be produced.

2.2.2 Activities of Data Management Plan

The DMP is a dynamic document, updated throughout the whole project lifecycle. The final version

of this report will be delivered by the end of the project, reflecting on lessons learnt and describing

the plans implemented for sustainable storage and accessibility of the data, even beyond the project’s

lifetime.

A Knowledge Management system will be developed, which incorporates in a structured way, the

technical and business knowledge created during the project. The activities of the Z-Fact0r

concerning the data management are planned as follows:

- Knowledge management – to be led by the DEM, in which the DMP will be delivered.

- A knowledge management document will be created, based on DMP, describing how the

acquired data and knowledge will be shared and/or made open, and how it will be maintained

and preserved. The identifiable project data will be provided in a manner to define the

relevant knowledge, increase partners’ awareness, validate the result, and timeframe of

actions.

- Technology watch - All partners will be responsible for periodically updating the knowledge

management system with outcomes of research work conducted by other groups and any

new patents/patent applications, i.e. to ensure that ongoing relevant technological

developments and innovations are identified, analysed, and hopefully built upon during the

course of the project.

2.2.3 Register on numerical datasets generated or collected in Z-Fact0r

The goal of the DMP is to describe numerical model or observation datasets collected or created by

Z-Fact0r during the runtime of the project. The register on numerical datasets has to be understood

as a living document, which will be updated regularly during the project´s lifetime.

The operational phase of the project started in October 2016, so there is no dataset generated or

collected until delivery date of this DMP (M6). However, this is not a fixed document so it will be

updated and augmented with new datasets and results during the duration of Z-Fact0r project.

The information listed below reflects the conception and design of the individual partners in the

different work packages at the beginning of the project. The data register will deliver information

according to the information detailed in Annex 1 of the GA document:

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Dataset reference and name: identifier for the dataset to be produced.

Dataset description: descriptions of the data that will be generated or collected, its origin or

source (in case it is collected), nature and scale and to whom it could be useful, and whether it

underpins a scientific publication. Information on the existence (or not) of similar data and the

possibilities for integration and reuse.

Partners activities and responsibilities: partner owner of the device, in charge of the data

collection, data analysis and/or data storage, and WPs and tasks it is involved.

Standards and metadata: reference to existing suitable standards of the discipline. If these do not

exist, an outline on how and what metadata will be created. Format and estimated volume of

data.

Data exploitation and sharing: description of how data will be shared, including access procedures

and policy, embargo periods (if any), outlines of technical mechanisms for dissemination and

necessary software and other tools for enabling re-use, and definition of whether access will be

widely open or restricted to specific groups. Identification of the repository where data will be

stored, if already existing and identified, indicating in particular the type of repository

(institutional, standard repository for the discipline, etc.) and if this information will be

confidential (only for members of the CCS) or public. In case a dataset cannot be shared, the

reasons for this should be mentioned (e.g. ethics, rules of personal data, intellectual property,

commercial, privacy-related, security-related).

Archiving and preservation (including storage and backup): description of the procedures that

will be put in place for long-term preservation of the data. Indication of how long the data should

be preserved, what is its approximated end volume, what the associated costs are and how these

are planned to be covered.

2.2.4 Metadata for Data Management

An initial plan of research data has been explored in Annex 1 of the GA. The dataset list is provided

in the table below, while the nature and details of each dataset are presented in the next section.

Table 1. Research data that will be collected and generated during Z-Fact0r.

Research Data Partners

Data structures with production machine signatures (healthy and deteriorated

conditions)

ATLANTIS

Machine Deterioration thresholds for predicting production of defected products ATLANTIS

RCA data structures for identifying the root cause of a defect in upstream stages CERTH/ITI

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Data from the comparative assessment (i.e. with and without Z-Fact0r) in the 3 use

cases: difference in production cost/waste/scrap, in detection efficiency, in single-

stage production defect rate, in average multistage production defect rate, in

production output quality (qualified output / total output produced), in defect

propagation to downstream stages

MICROSEMI,

INTERSEALS,

DURIT

Defect detection efficiency data: false alarm rate, precision, recall, F-Measure ALL PARTNERS

Defect prediction efficiency data: positive prediction rate ALL PARTNERS

Discrete Event Modelling – cost function generation to optimize production with

green scheduling

BRUNEL

Validation and verification of KPIs to assess the direct impact of system level to

the final cost

BRUNEL, EPFL

Context aware models and associated algorithms EPFL

Additive manufacturing methodologies for rework and repair CETRI

Improved functionalities of i-LiKe knowledge management and DSS suite HOLONIX

Partners will characterize their research data and associated software and/or used in the project

whether these are discoverable, accessible, assessable and intelligible, useable beyond the project’s

life and interoperable. In specific, research data can be discovered by means of an identification

mechanism such as Digital Object Identifier and accessible by defining modalities, the scope of

the action, establish the licenses and define the IPR.

Otherwise research data will be assessable and intelligible allowing third parties to make

assessments. Also, the dataset will be useable beyond the original purpose for which it was collected

or usable to third parties after the collection of the data for long periods (repositories, preservation

and curation). Finally, research data will offer interoperability to specific quality standards and allow

data exchange between researchers, institutions, organizations, countries, re-combinations with

different datasets, data exchange, compliant with available software applications.

2.2.5 Data description

In order to collect the information about the research data that will be generated in different activities

of the Z- Fact0r project, we have elaborated a template to be completed by the consortium partners.

This template includes the following information items:

Dataset reference and name: name, homepage, publisher, maintainer

Dataset description: description, provenance, usefulness, similar data, re-use and integration

Standards and metadata: metadata description, vocabularies and ontologies

Data sharing: license, URL dataset description, openness, software necessary, repository

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Archiving and preservation: preservation, growth, archive, size

2.2.5.1 Dataset per partner

All partners have identified the data that will be produced in the different project activities;

DS.CETRI.Z-Repair_AM_processing

Data Identification

Dataset description Formulation of the inks or paste for additive manufacturing

repairing processes & printing/deposition protocols.

Source Various characterization techniques, e.g. microscopy, viscometer,

printing station.

Partners activities and responsibilities

Partner owner device CETRI

Partner in charge of

data collection

CETRI


data analysis

CETRI


data storage

CETRI

WPs and tasks T2.4 in WP2

Standards

Info about metadata

(Production and

storage dates, places,

and documentation)

The metadata include:

a) The characteristics of the materials to be deposited.

b) The user requirements as obtained by the end users.

Standards, Format,

Estimated volume of

data.

No standards apply. The format will be in the form of

spreadsheets and images (TIFF of JPG). Estimated volume is <

10 MB.

Data exploitation and sharing

Data exploitation

(purpose/use of the

data analysis)

The results of the study have the potential to be exploited by

CETRI along with the Z-Fact0r end users MICROSEMI,

DURIT and INTERSEALS, as well as by SIR, towards the

implementation of integrating new processes in their production

lines.

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Data access policy /

Dissemination level

In general, the data will be confidential with the exception of

possible future publications in the case the consortium permits

such activities.

Data sharing, re-use

and distribution

CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users

(MICROSEMI, DURIT, INTERSEALS). Each set will be shared

with the individual partners in the form of raw data and complete

reports in order to receive feedback during the project

implementation.

Embargo periods

No

Archiving and preservation (including storage and backup)

Data storage (including

backup)

Data will be stored into a computer and an external hard disc and

will be send frequently to the individual end-users. The data will

be stored permanently in a computer in CETRI facilities.

DS.CETRI.Z-Repair_laser_processing

Data Identification

Dataset description Measurements of the laser source couples to measurements of the

processed surface.

Source Laser source. Laser Power Meter Microscopy.


Partner owner device CETRI


data collection

CETRI


data analysis

CETRI


data storage

CETRI


Standards

Info about metadata

(Production and

The metadata include:

a) The type/origin of the processed material.

b) The conditions of the experiments.

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and documentation)

Standards, Format,

Estimated volume of

data.

No standards apply. The format will be in the form of

spreadsheets and images (TIFF of JPG). Estimated volume is <

10 MB.


Data exploitation

(purpose/use of the

data analysis)

The results of the study have the potential to be exploited by

CETRI along with the Z-Fact0r end users MICROSEMI,

DURIT and INTERSEALS, as well as by SIR, towards the

implementation of integrating new processes in their production

lines.


Dissemination level

In general, the data will be confidential with the exception of

possible future publications in the case the consortium permits

such activities.


and distribution

CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users

(MICROSEMI, DURIT and INTERSEALS). Each set will be

shared with the individual partners in the form of raw data and

complete reports in order to receive feedback during the project

implementation.

Embargo periods

No



backup)

Data will be stored into a computer and an external hard disc and

will be send frequently to the individual end-users.

The data will be stored permanently in a computer in CETRI

facilities.

DS.DURIT. Production line. Demo 3.

Data Identification

Dataset description Data collected:

Dimensions/shapes/surface and 3D details

Superficial defects like cracks

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Source The data will be collected by different sensors and imaging

devices such as cameras. Installed in the production line after

green machining and after finishing operations. Ideally installed

in machine for real time inspection although very difficult to

implement at the current time.


Partner owner device DURIT


data collection

DURIT


data analysis

DURIT


data storage

DURIT

WPs and tasks The data are going to be collected in WP5 and WP6.

Standards

Info about metadata

(Production and


and documentation)

The dataset will be accompanied by information regarding:

Drawings and sequence of operations.

Batch of material used.

Operators involved.

Date, time.

Temperature and relative humidity in the metallurgy section.

Standards, Format,

Estimated volume of

data.

Our tests will be in a specific type of pieces. The volume of data

depends of the quantity of order.


Data exploitation

(purpose/use of the

data analysis)

Production process recognition and help during the different

production phases, avoiding mistakes. Support of quality checks

and production batches recalls


Dissemination level

The full dataset will be confidential and only the members of

the consortium will have access on it. Furthermore, if the

dataset or specific portions of it (e.g. metadata, statistics, etc.)

are to become of widely OA, a data management portal will be

created that should provide a description of the dataset and link

to a download section. Of course, these data will be

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anonymized, so as not to have any potential ethical issues with

their publication and dissemination.


and distribution

Data sharing is dependent of DURIT, DURIT´s customers and

partner requirements.

Embargo periods

None



backup)

All information belongs to the industrial partner that owns the

shop floor. All data will respect the partner policies. All data has

to be stored till the end of life/warranty of the produced

component. Probably also stored at DURIT servers at the cloud.

DS.EPFL.01_KMDSS

Data Identification

Dataset description Z-Fact0r Knowledge Management and Decision Support System

Dataset.

Source Device Manager, Event Manager, Semantic Context Manager, Z-

Fact0r Repository.


Partner owner device The device will be owned by Z-Fact0r End-users (MICROSEMI,

INTERSEALS, DURIT), where the data collection will be

performed.


data collection

Various partners related to the specific event and/or operation.


data analysis

Various partners related to the specific event and/or operation.


data storage

EPFL will store data related to KMDSS (various partners can

handle the rest of the data).

WPs and tasks The data will be collected within the activities of WP2, WP3 and

WP4.

Standards

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Info about metadata

(Production and


and documentation)

Indicative metadata include: Input from the Sensor Network

(through the Device Manager), the overall model of Production

activities (through Z-Fact0r Repository), shop-floor events data

from the Event Manager, and context – aware knowledge

stemming from the Semantic Context Manager (Ontology).

Standards, Format,

Estimated volume of

data.

Data can be available in XML or JSON format. Estimation of the

volume of data cannot be predicted in advance of a real use of

the technology at the shop floor level.


Data exploitation

(purpose/use of the

data analysis)

The collected data will be used for better understanding of the

processes and activities evolving in the shop-floor which will

provide actionable knowledge in the form of a set of

recommendations to (i) supervise and provide feedback for all the

processes executed in the production line, (ii) evaluate

performance parameters and responding to defects, keeping

historical data, (iii) send efficiently alarms to initiate actions, filter

out false alarms, increase confidence levels (through previously

acquired knowledge) of early defect detection and prediction, etc.


Dissemination level

Accessible to Z-Fact0r consortium members including the

commission services as defined in the Z-Fact0r GA.


and distribution

The sharing of this data is yet to be decided together with the

industrial partners.

Embargo periods

None



backup)

Data will be stored in a dedicated repository.

DS.EPFL.02.SemanticContextManager

Data Identification

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Dataset description Context-aware shop-floor analysis and semantic model for the

annotation and description of the knowledge to represent

manufacturing system performance.

Source Z-Fact0r repository.


Partner owner device Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT)


data collection

EPFL


data analysis

EPFL


data storage

EPFL

WPs and tasks The data will be collected within the activities of WP3 and in

particular T3.5.

Standards

Info about metadata

(Production and


and documentation)

Data from Z-Fact0r repository (data concerning machines,

workers, actors, activities and processes, production data logs,

etc.).

Standards, Format,

Estimated volume of

data.

Generated output will be the semantic enrichment of shop-floor

data for representation of processes, actors, alarms, actions,

work-pieces/products, etc., e.g. as RDF Triplets. Standards:

W3C-OWL, RDF. Less than 2GB.


Data exploitation

(purpose/use of the

data analysis)

Data is required for the Z-Fact0r ontology development.

Ontology describes semantic models. The ontology will be used

in order to drive the semantic framework. Furthermore, it will be

used for data integration, visualization, inferencing /reasoning.

The ontology will describe the basic entities of the project and

model relevant structures of multi-stage manufacturing

processes.


Dissemination level

Accessible to Z-Fact0r consortium members including the

commission services as defined in the Z-Fact0r GA.

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and distribution

The Ontology will be uploaded in a server where it will be

accessible to Z-Fact0r consortium members including the

commission services.

Embargo periods

None



backup)

Data will be stored in a dedicated repository. No expiry date – revisions will be kept.

DS.HOLONIX.ProductionManagement

Data Identification

Dataset description Collections of data from industrial partner’s production plant,

operators, and elaborated data from other Z-Modules. These

collections of data contain information about machine

conditions, plant conditions, process KPIs of an Industrial

production plant.

Source Industrial partners’ production plant with its operators and other

Z-Modules.


Partner owner device Industrial partners


data collection

Industrial partner with support of HOLONIX presumably


data analysis

HOLONIX and Z-Modules


data storage

HOLONIX


Standards

Info about metadata

(Production and


and documentation)

A set of RESTful APIs will be released with documentation of

how to require data from datasets.

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Standards, Format,

Estimated volume of

data.

No estimation has been done so far.


Data exploitation

(purpose/use of the

data analysis)

Support on the monitoring of production machine, production

performance and process both for operators and other

monitoring modules.


Dissemination level

Collections of data of Production management module should be

accessible only for Z-Fact0r consortium partners only.


and distribution

Data sharing should not be possible with users outside of the

project. A set of RESTFul APIs will be implemented to share data

between partners.

Embargo periods

None



backup)

Physical place to store production data has still to be decided, data

will be stored at least for all the duration of the project.

DS.HOLONIX.Repository

Data Identification

Dataset description The repository is a collection of dataset coming from various

sources including sensors, operator notes, production line

installed at industrial partners of the project as well as data

incoming from Z-modules as results of their calculation.

Source Z-Fact0r industrial partner’s production plant and Z-modules.


Partner owner device Z-Modules’ responsible partner and industrial partners.


data collection

Z-Modules and industrial partners.


data analysis

Various Z-modules

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data storage

HOLONIX

WPs and tasks T3.2

Standards

Info about metadata

(Production and


and documentation)

A set of RESTful APIs will be released with documentation of

how to require data from datasets.

Standards, Format,

Estimated volume of

data.

JSON will be data exchange format between Repository and Z-

Modules. No estimation of data volume has been done so far.


Data exploitation

(purpose/use of the

data analysis)

The datasets collected should be used by various modules of the

project for the pursue of Zero defects production objective.


Dissemination level

Still to be clarified, for the nature of the dataset collected, only

the members of consortium should have rights to access to the

datasets with appropriate authorization/authentication policy.


and distribution

No discussion about this matter has been done so far, and should

not be shared with entities outside of Z-Fact0r consortium.

Embargo periods

None



backup)

Still to be decided where the collected datasets will be stored

definitely.

DS.CONFINDUSTRIA.Events&Roadmapping

Data Identification

Dataset description Info regarding the demand for Zero Defect production and a

global matching coming from a Desk Research based on

technology brokerage system available at CONFINDUSTRIA.

List of potential tradefairs and events.

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21

List of potential customer/visitors of workshop and potential

companies interested in Z-Fact0r technology.

Source Internet, specific DBs (we have not selected anyone).


Partner owner device CONFINDUSTRIA


data collection

CONFINDUSTRIA


data analysis

CONFINDUSTRIA


data storage

CONFINDUSTRIA

WPs and tasks WP 7: T7.3 Roadmap for wider adoption and take-up

WP 8: T8.2 Adoption Plan for increasing Awareness

WP 9: T9.2 To identify the relevant conference or event

Standards

Info about metadata

(Production and


and documentation)

It will be used the only available and not confidential (public) data

and documentation coming from Z-Fact0r results in order to

define our strategy and desk research

Standards, Format,

Estimated volume of

data.

--


Data exploitation

(purpose/use of the

data analysis)

It will be used the only available and not confidential (public) data

and documentation coming from Z-Fact0r results in order to

define our strategy and desk research


Dissemination level

It will be respected the project rules about confidentiality, by

using and disseminate the only public data

Our results will be public


and distribution

Our results will be public, they could be shared and re-used as a model:

Research method structure

Roadmap structure

Business Network created

Embargo periods None

FOF-03-2016 Z-Fact0r - 723906

22



backup)

Original data and results will be kept in our company server at

least until the all project duration and audit period. Results will be

also shared with partners and kept in the project repository.

DS.ATLANTIS.ES-DSS

Data Identification

Dataset description Dataset for insufficient glue detection obtained by cameras and

lasers at the glue implementation machine.

The camera images will be processed and not saved anywhere,

while the metadata of insufficient glue placement will be used for

analysis and detection.

Data will be used for early detection of failures.

The metadata will be able to send notifications and alarms to the

responsible control operators and glue workers.

Source The dataset will be collected by using cameras and lasers at the

glue machine


Partner owner device The device will be owned to the industry (MICROSEMI), where

the data collection is going to be performed.


data collection

Various partners related to the specific incident and/or operation.


data analysis



data storage

ATLANTIS will store data related to ES-DSS (various partners

can handle the rest of the data).

WPs and tasks The data are going to be collected within activities of WP3 and

more specifically within activities of T3.1, T3.2, T3.3 and T3.4.

Standards

Info about metadata

(Production and

The dataset will be accompanied with a detailed documentation

of its contents. Indicative metadata include:

FOF-03-2016 Z-Fact0r - 723906

23


and documentation)

(a) description of the experimental setup (e.g. location, date, etc.)

and procedure that led to the generation of the dataset,

(b) annotated detection of insufficient glue, activity, business

process, state of the monitored activity.

Standards, Format,

Estimated volume of

data.

The data will be stored at XML format and are estimated to be

1GB per day.


Data exploitation

(purpose/use of the

data analysis)

The collected data will be used for the development of the

activities analysis and incident detection methods of the Z –

Fact0r project and all the tasks, activities and methods that are

related to it.


Dissemination level

The full dataset will be confidential and only the members of the

consortium will have access on it.


and distribution

The sharing of this data is yet to be decided along with the


Embargo periods

None



backup)

Data will be stored in a DB. RAID and other common backup

mechanism will be utilized to ensure data reliability and

performance improvement and to avoid data losses.

DS.ATLANTIS.Evaluation

Data Identification

Dataset description Values of the KPIs for:

1) Technical indicators.

2) User/Stakeholders acceptance.

3) Indicators for accessing the impact of the project on the

factories.

Source The dataset will be collected from Z-Fact0r industrial partners,

technology providing partners and User/Stakeholders - the

tool/solution beneficiaries.

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24


Partner owner device The device will be owned by the consortium.


data collection

ATLANTIS with the respective and responsible partners per

toolkit/task/plant.


data analysis

ATLANTIS.


data storage

ATLANTIS will store analysed data related to Solution

Evaluation.

WPs and tasks The data are going to be collected through demonstrations in

relevant environment, specifically within T5.3 activity in

collaboration with WP6.

Standards

Info about metadata

(Production and


and documentation)

Collected data from the execution of the demonstrations at the

operational environment of the pilot sites (WP6) as well as the

users’ acceptance and overall impact will be analysed and

documented - Report on Solution Validation.

Standards, Format,

Estimated volume of

data.

Alphanumeric


Data exploitation

(purpose/use of the

data analysis)

Solution validation will be synthesised and documented in the

form of report - deliverable.


Dissemination level

The full dataset will be confidential, the reports will be public.


and distribution

Data will be shared among involved partners.

Embargo periods

None



backup)

To avoid data losses during the project and to ensure data

reliability analysed data will be stored for up to two years after the

project life by ATLANTIS.

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DS.ATLANTIS.ReverseSupplyChain

Data Identification

Dataset description Dataset for gathered data during the manufacturing process,

obtained by cameras, lasers and other measurement sensors.

The camera images will be processed and not saved anywhere

while the metadata for all the other sensors will be used for

analysis in order to activate the Reverse – flow process in the

Reverse Supply Chain.

Data will be used for defect detection in the reverse supply chain.

The metadata will be able to send notifications and alarms to the

responsible machine operators for removal the defected parts,

special inspection, return to previous internal tier (upstream

stage) or external tier (other production line or external supplier).

Standards and prototypes shall be included in the data for

comparison with the defected parts and setting acceptance levels.

Source Cameras, lasers and measurement instruments at different points

of the production lines


Partner owner device The device will be owned to the industry, where the data

collection is going to be performed.


data collection



data analysis

ATLANTIS will analyse the data in order to provide answers and

reliable use of the Reverse Supply Chain.


data storage

ATLANTIS will store data related to Reverse Supply Chain

(various partners can handle the rest of the data).

WPs and tasks The data are going to be collected within activities of WP2 and

more specifically within activities of T2.5.

Standards

Info about metadata

(Production and


and documentation)

The dataset will be accompanied with a detailed documentation

of its contents. Indicative metadata include:

(a) description of the experimental setup (e.g. location, date, etc.)

and procedure that led to the generation of the dataset,

FOF-03-2016 Z-Fact0r - 723906

26

(b) annotated detection of a defective part in the production line,

the cause of the defect, the acceptable standards and limits of the

part, as well as return point in the production process.

Standards, Format,

Estimated volume of

data.

The data will be stored at XML format and are estimated to be

100ΜΒ per day.


Data exploitation

(purpose/use of the

data analysis)

The collected data will be used for the development of the

activities analysis and defect detection methods in the production

lines of the Z – Fact0r project plants and all the tasks, activities

and methods that are related to it. The Reverse Supply Chain shall

be able to use the data in order to decide whether or not a

defective part should return to a previous tier.


Dissemination level

The full dataset will be confidential and only the members of the

consortium will have access on it.


and distribution

The sharing of this data is yet to be decided along with the

industrial partners

Embargo periods

None



backup)

Data will be stored in a DB. RAID and other common backup

mechanism will be utilized to ensure data reliability and

performance improvement and to avoid data losses.

DS.DATAPIXEL.3DPointcloud

Data Identification

Dataset description High accuracy and high resolution 3D Pointclouds of scanned

parts. The Pointcloud is a list of 3D points, and can be structured

and unstructured

Source DATAPIXEL 3D Scanner


Partner owner device DATAPIXEL

FOF-03-2016 Z-Fact0r - 723906

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data collection

DATAPIXEL


data analysis

DATAPIXEL


data storage

DATAPIXEL and Z-Fact0r repository

WPs and tasks WP2 and WP3

Standards

Info about metadata

(Production and


and documentation)

Metadata includes part identification, date and time of data

collection, equipment. Pointcloud is part of the information

associated with the manufactured parts.

Standards, Format,

Estimated volume of

data.

ASCII list of X Y Z is the most common format. Typically,

Pointclouds have a size between 100 K to 10M points, or

3Mbytes to 300 Mbytes.


Data exploitation

(purpose/use of the

data analysis)

Main use will be the automatic detection of defects by two

methods: CAD based inspection and GD&T analysis.


Dissemination level

Confidential, except parts authorized by the industrial partners.


and distribution

The data will be shared using the Sensor network manager, and

stored in the repository for further future analysis.

Embargo periods

None



backup)

Data will be stored in the Z-Fact0r repository and by the 3D

Pointcloud analysis software.

DS.DATAPIXEL.CADModel

Data Identification

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Dataset description The CADModel is the description of the surfaces and geometries

of the designed part. It is representing the 3D information of the

manufactured part model, and will be utilized for CAD based

inspection.

Source (e.g. which

device?)

Industrial partner’s CAD modelling software and DATAPIXEL

3D Pointcloud Analysis software.


Partner owner of the

device

Industrial partners (MICROSEMI, INTERSEALS, DURIT) and

DATAPIXEL.

Partner in charge of the

data collection

(if different)

Same


data analysis

(if different)

DATAPIXEL


data storage

(if different)



Standards

Info about metadata

(Production and

storage dates, places)

and documentation?


generation. CAD Model is part of the information associated with

the manufactured parts.

Standards, Format,

Estimated volume of

data.

STEP format


Data exploitation

(purpose/use of the

data analysis)

Main use will be the automatic detection of deviations based in

local regions and the extraction of nominal values for GD&T

analysis.


Dissemination level


FOF-03-2016 Z-Fact0r - 723906

29

(Confidential, only for

members of the CCS) /

Public


and distribution

(How?)

The data will be shared by the 3D Pointcloud Analysis module,

and stored in the repository for further future analysis.

Embargo periods

(if any)

None



backup): where? For

how long?



DS.DATAPIXEL.DeviationMaps

Data Identification

Dataset description The deviation map is a 3D representation of surface deviations

calculated between a captured Pointcloud and the reference CAD

model. The deviation map is represented as a list of regions with

their corresponding deviation. Typically, the regions are

polygonal regions with their associated deviation.

Source DATAPIXEL 3D Pointcloud Analysis software.




data collection

DATAPIXEL


data analysis

DATAPIXEL


data storage

DATAPIXEL and Z-Fact0r repository.


Standards

FOF-03-2016 Z-Fact0r - 723906

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Info about metadata

(Production and


and documentation)


generation. Deviation Map is part of the information associated

with the manufactured parts.

Standards, Format,

Estimated volume of

data.

A polygonal mesh with an associated deviation number in mm.

Most common formats are STL with annotated deviations and

PLY. Typically, deviation maps have a size between 100 K to 1M

polygons, or 10Mbytes to 100 Mbytes.


Data exploitation

(purpose/use of the

data analysis)

Main use will be the automatic detection of defects based in local

deviations. A deviation threshold can be defined to identify

defects.


Dissemination level



and distribution



Embargo periods

None



backup)



DS.DATAPIXEL.MeasurementPlan

Data Identification

Dataset description The MP is a definition of the GD& to be measured in the

Pointcloud. The MP contains a detailed definition of geometrical

elements and the tolerances associated to them. This information

is the input to the Geometrical Feature Extraction module of the

3D Pointcloud Analysis software

Source DATAPIXEL 3D Pointcloud Analysis software. Normally the

MP is extracted from the geometrical information contained in

the CAD model


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data collection

DATAPIXEL


data analysis

DATAPIXEL


data storage



Standards

Info about metadata

(Production and


and documentation)

Metadata includes project identification, date and time of data

generation. MP is part of the project information.

Standards, Format,

Estimated volume of

data.

The standard format for MP can be QIF or DMO.


Data exploitation

(purpose/use of the

data analysis)

Main use will be automatic measurement of dimensions and

geometries based in nominal values and tolerances. This

information will be used for defect detection and process analysis.


Dissemination level



and distribution



Embargo periods

None



backup)



DS.DATAPIXEL.MeasurementResults

Data Identification

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32

Dataset description The Measurement Results are the set of measurement values

extracted from the Pointcloud based in the MP.

Source DATAPIXEL 3D Pointcloud Analysis software.




data collection

DATAPIXEL


data analysis

DATAPIXEL


data storage

DATAPIXEL and Z-Fact0r repository.


Standards

Info about metadata

(Production and


and documentation)


generation. Measurement Results is part of the information

associated with the manufactured parts.

Standards, Format,

Estimated volume of

data.

The standard format for MP can be QIF or DMO.


Data exploitation

(purpose/use of the

data analysis)

Main use will be the automatic detection of based in geometrical

deviations.


Dissemination level



and distribution



Embargo periods

None


FOF-03-2016 Z-Fact0r - 723906

33


backup)



DS.CERTH/IRETETH.DataConditioning

Data Identification

Dataset description Data collected by DATAPIXEL’s laser system or other

complementary data sources for defect detection.

Source Should be defined by DATAPIXEL.




data collection

DATAPIXEL + the relevant end user (manufacturer) depending

on the use case.


data analysis

IRETETH/CERTH


data storage

TO BE DEFINED

WPs and tasks WP2 / T2.1 - T2.2

Standards

Info about metadata

(Production and


and documentation)

Should be discussed between DATAPIXEL and

IRETETH/CERTH.

Standards, Format,

Estimated volume of

data.

Should be determined by DATAPIXEL. In IRETETH/CERTH

we are open to use different data formats with a preference in raw

data formats. As far as the volume, the more the better. Ideally,

we would like to have hundreds of measurements per product

(e.g. 500 per case including defected and non-defected).


Data exploitation

(purpose/use of the

data analysis)

This should be discussed within the relevant partners.


Dissemination level

This should be discussed within the consortium and approved by

the DEM.

FOF-03-2016 Z-Fact0r - 723906

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and distribution

This should be discussed within the relevant partners.

Embargo periods

None



backup)

We need to see who is responsible for the data storage task.

DS.SIR.RoboticCellData

Data Identification

Dataset description Collections of data from SIR robotic deburring cell. These

collections of data contain information about machine

conditions, algorithms, machine data, plant conditions, process

KPIs.

Source SIR robotic deburring cell.


Partner owner device SIR


data collection

SIR with support of technological partners involved in the task.


data analysis

SIR with support of technological partners involved in the task.


data storage

SIR


Standards

Info about metadata

(Production and


and documentation)

--

Standards, Format,

Estimated volume of

data.

Mainly consisting in MS documents released using the following

formats (.doc, .pptx and .xls files, images for visualizing and

conceptualizing the use cases will be released as PDF files), UML

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documents, machine algorithms (various type of informatics

languages C#, RAPID, etc).


Data exploitation

(purpose/use of the

data analysis)

The datasets collected should be used by SIR to achieve the

objectives of T2.3.


Dissemination level

Confidential


and distribution

No discussion about this matter has been done so far, and should

not be shared with entities outside of Z-Fact0r consortium.

Embargo periods

None



backup)

Documents will be store in FREEDCAMP document

management system. Machine data, algorithms and machines

backups will be store in the SIR internal repository.

DS.SIR.IndustrialPartnersData

Data Identification

Dataset description Collections of data from industrial partners. These collections of

data contain information about machine conditions, plant

conditions, process KPIs of an Industrial production plant.

Source Industrial partners’ production plant, internal reports, operators.


Partner owner device Industrial partners.


data collection

Industrial partner with support of SIR.


data analysis

Task leader


data storage

Task leader and SIR

WPs and tasks WP1

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Standards

Info about metadata

(Production and


and documentation)

--

Standards, Format,

Estimated volume of

data.

Mainly consisting in MS documents released using the following

formats (.doc, .pptx and .xls files, images for visualizing and

conceptualizing the use cases will be released as PDF files) and

UML documents. The metadata standard proposed is the CERIF.


Data exploitation

(purpose/use of the

data analysis)

Only for members of the CCS.


Dissemination level

The information leading to the preparation of the following

deliverable might be confidential as the following deliverables are

marked as confidential:

D1.1 Z-Fact0r User requirements DURIT M3

D.1.3 Z-Fact0r system architecture EPFL M5

D1.5 Report on Z-Fact0r strategy implementation and risk

analysis EPFL M18


and distribution

For the time being data are expected to be used internally as input

by the other WPs. However, D1.2 Report on the analysis of SoA,

existing and past projects initiatives due by CERTH at M2 and

D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are

expected to be released publicly.

Embargo periods

None



backup)

Documents are stored in FREEDCAMP document management

system. Data and documents will be up to five years after the

project completion. Revisions will be stored in the

FREEDCAMP document management system.

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2.2.5.2 Dataset per task

In addition, data information that will be generated in the different tasks, has been identified by the

different partners;

Task: T1.1- T1.5, T2.1-T2.5, T6.1-T6.3

WP: WP1 + WP2 + WP6 WP Leader: SIR

Author: G. Tinker (MICROSEMI)

1) Scope

State the purpose of the data generation/ collection

Main aims for MICROSEMI are for the improvement of the dispense process and its

analysis. Other opportunities for the system and the data might be learning how much glue

might be needed for a new size die (prediction) and checking LCP panels for surface defects

prior to dispensing.

Explain the relation to the objectives of the project/WP/Task

The data being collected will enable the KPIs to be monitored and to generate history for

prediction and correction of the process.

2) Types

Are the data digital/hard copies or both?

Digital

What types of data will the WP generate/collect? Specify the types and formats of data

generated/collected (for example .xls files, .ppt files, emails, .doc files)

MICROSEMI preference for data would be:

o xls for sensor history data

o xls for a volumetric measurement of the glue dispensed

o jpeg images of the surface

Is the data generated or collected from other sources under certain terms and conditions?

TBC – not believed to be a requirement at this stage

How is generated/collected? Specify the origin of the data and instruments/tools that will

be used.

TBC – Possibilities of either Pointcloud clouds from DATAPIXEL or micro – profilometry

data generated by CERTH or both may need to be utilised

State the expected size of the data (if known)

Currently unknown but Good IT infrastructure at MICROSEMI means Data size should

not be a constraint

Standards

None

3) Ownership

Is another organization contributing to the data development?

TBC – If the answer does end up being yes, it will be a member of the Z-Fact0r project

4) Reuse of existing data

Specify if existing data is being re-used (if any)

No Data is currently being collected other than the process improvement project that has

already been completed.

5) Data use

FOF-03-2016 Z-Fact0r - 723906

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How will this data be exploited and/or shared/made accessible for verification and re-use?

Outline the data utility: to whom will it be useful

Much depends on:

Who wants access?

How often they want access?

How big the files are?

MICROSEMI does have an FTP site – terms of access to this will have to be agreed by

MICROSEMI and the members of the Z-Fact0r project.

6) Dissemination Level of Data

Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have

access?

TBC – this depends a little on the data being collected and if it is deemed sensitive.

7) Storage and disposal

How will this data be stored?

Probably on a local PC with the option to back up data (depending on size) to the

MICROSEMI servers at Caldicot.

How long is it required to keep the data? Expire date. Will revisions be kept?

Duration of the project, and potentially five years after the completion of the project.

Task: T1.1 -T1.5

WP: 1 USER REQUIREMENTS – SPECIFICATIONS – USE CASE ANALYSIS

WP Leader: SIR

Author: Marcello Pellicciari (SIR)

1) Scope


Qualitative and quantitative data will be produced:

I. WP1 data generated and collected are aimed at defining both the user and system

requirements and use cases (T.1.1 and T.1.4)

II. Bibliographic and data-based information (e.g. Cordis) for T.1.2 State of the art

to analyse new, live and past projects, initiatives in the field.

III. Workflow and UML diagrams, blue prints will be generated to design the Z-

Fact0r architecture (T.1.3)

IV. Report on Z-Fact0r strategy and risk analysis (T.1.5) to monitor the status of the

manufacturing process in real time.


Data are related to all tasks WP1. (See above)

2) Types


Digital data and documents will be produced.



Data are preserved in their incoming format, Files generated and used will be mainly

consisting in MS documents released using the following formats (.doc, .pptx and .xls files,

images for visualizing and conceptualizing the use cases will be released as PDF files).


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No


be used.

Unified Modelling Language will be used


Not yet a clear idea.

Standards

Not at the moment.

3) Ownership


To date no other external organization is contributing to the data development activities of

WP1.



For the time being data are expected to be used internally as input by the other WPs.

However, D1.2 Report on the analysis of SoA, existing and past projects initiatives due by

CERTH at M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are expected to

be released publicly.

5) Data use



D1.2 Report on the analysis of SoA, existing and past projects initiatives due by CERTH at

M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are expected to be released

publicly.



access?

The information leading to the preparation of the following deliverable might be confidential

as the following deliverables are marked as confidential:

o D1.1 Z-Fact0r User requirements DURIT M3

o D.1.3 Z-Fact0r system architecture EPFL M5

o D1.5 Report on Z-Fact0r strategy implementation and risk analysis EPFL M18



Documents are stored in FREEDCAMP document management system. Content creator

upload the relevant file.


Data and documents will be up to five years after the project completion. Revisions will be

stored in the FREEDCAMP document management system.

Task: T.1-USER REQUIREMENTS

WP: WP1 + WP6 WP Leader: SIR

Author: E. Soares (DURIT)

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1) Scope


Automated quality control, with high accuracy level and predictive system for defect

generation based on online continuous monitoring.


The data collected will enable to detect probability or trends that lead to defects that normally

result in scrapping the parts.

2) Types


Both



o xls for sensor history data

o jpeg images of the defects


Possibly collected by sensors at an bench top apparatus.


be used.

Optical and physical sensors to be studied.


A few MB per type of part. Perhaps 1 GB per day.

Standards

3) Ownership


Only partners from Z-Fact0r



No Data is currently being collected

5) Data use



System software, cloud where DURIT servers are stored and some local pc. data will be used

mainly by Quality Department



access?

Partners from Z-Fact0r can have access during the project. In our premises access is limited

to quality operators.



Probably on a local PC + DURIT servers at the cloud.


five years minimum

Task: T1.4, T6.2

WP: WP1 (User Requirements), WP6 (Demonstration activities)

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WP Leader: SIR, INTERSEALS

Author: Pierino Izzo (INTERSEALS)

1) Scope



The INTERSEALS actual data are generated and exploited to plan and manage the

Customer orders, to planning the production, getting feedback by the production phase,

managing of the maintenance.

The Z-Fact0r will useful for all the objectives of the software itself: Z-DETECT, Z-

PREDICT, Z-PREVENT, (Z-REPAIR), Z-MANAGER.

This data could then be connected to the INTERSEALS ERP and Quality Data

Management.

2) Types


The data are digital.


generated/collected (for example .xls files, .ppt files, emails, .doc files).

They could be of two kinds: .xls, SQL formats, emails.


For Z-Fact0r, there isn’t this possibility.


be used.

The data for Z-Fact0r will be generate by:

FT-IR (Infrared spectroscopy): for material checking.

System control (CoMo by Kistler) that concentrate the data from the sensor cavities

pressure.

Injection moulding machine parameters: these data can be achieved by the

connection to the PLC of the injection Machine (the PLC can be Siemens, Omron,

Moog).

Data from visual and dimensional checking machine (DATAPIXEL will be involved

in this)

Data from the worker that work beside the production cell and will communicate

with the software using Augmented Reality.


At the moment, our servers are of about 150Gbyte.

Standards

SQL server

3) Ownership


No, we have all the competence to generate and manage the data.



The existing data are re-used as really useful:

for making quotations

for process study

quality control

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traceability

claim answer

5) Data use



See point 4



access?

The data could be accessible after signing the INTERSEALS NDA.



Workstation server, SQL server


At least for 6 months for the dynamic production data and five years for the static data.

Task: Task 1.3

WP: WP 1

WP Leader: SIR

Author: EPFL

1) Scope

State the purpose of the data generation/collection

Development of the architecture of the Z-Fact0r system (i.e. functional view, information

view, deployment view, etc.) & the definition and description of the main components


A complete description of the modules included in the detailed view is provided in order to

point out the responsibilities of each module and their interactions with the global System

Architecture

2) Types


Digital



Emails, doc files, .vpp files etc.


No


be used.

Visual Paradigm V14.0 for component diagrams


Not known

Standards

UML for component diagrams

3) Ownership


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ALL Z-Fact0r partners



No

5) Data use



As described in GA document.



access?

As described in GA document. Accessible to Z-Fact0r consortium members including the

commission services. Based on further discussions and agreement between partners, part of

data (e.g. overall approach and architecture etc.) could be published in the form of an article

or conference proceedings for dissemination purposes.



All the collected info/data will be delivered in the deliverable D1.3


Duration of the project, and potentially five years after the completion of the project for

further research; but it will be with consent of the consortium members in case the data is

to be accessed and used for the purpose of academic exercise (e.g. teaching and publications)

Task: Task 1.5

WP: WP 1

WP Leader: SIR

Author: EPFL

1) Scope


Monitoring the application of the various Z-Fact0r strategies and risk analysis will determine

how well & successful the implementation of the strategies will be in the use cases, aligned

with the project objectives.


Data collected and generated will support part, machine and process level itself continuous

monitoring in real time. Actions of correctness will be suggested in case of error occurrence.

Re-evaluations of the deployed strategies will be conducted. Also, manufacturing equipment,

part and process status measurement analysis will be adapted to provide the means for

process validation. Z-Fact0r strategies developed according to objectives.

2) Types


Digital



Data types could be: .doc files, emails, SQL DB programs, .XML files etc.


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be used.

Machine sensors, network infrastructure /middleware (device manager)/ shop-floor (Z-

Fact0r repository for machine processes, part condition and worker’s actions)


Not known

Standards

3) Ownership


ALL Z-Fact0r partners



Data will be reused for corrective actions on the deployed strategies and actions will be

suggested based on correlations by the automatic decision support mechanism.

5) Data use



As described in GA document.



access?

Confidential, only for members of the consortium, Commission Services





Task: Task 2.2

WP: 2

WP Leader: EPFL

Author: IRETETH/CERTH

1) Scope


Data needed for formulation of data driven model.


Defect prediction from process inputs, correlated to Z-DETECT Module.

2) Types


Digital



Can accept data in any format (*.csv, *.xls, etc). Data output will be in the form of matlab

files, (*.mat, *.m, etc).


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Data are collected from the manufacturing processes (end users’ collection systems)


be used.


Standards

3) Ownership


No



No

5) Data use



As described on GA document.



access?

Confidential, only for members of the consortium, CCS.





Task: Task 2.5

WP: WP 2

WP Leader: EPFL

Author: EPFL

1) Scope


Supervise and provide feedback for all the processes executed in the production line,

evaluating performance parameters and responding to defects, keeping historical data. Send

efficiently alarms to initiate actions, filter out false alarms, increase confidence levels

(through previously acquired knowledge) of early defect detection and prediction.


KMS refers to an information and communication technology system for managing

knowledge in organizations for supporting creation, capture, storage and dissemination of

information. Facilitate the adoption of risk-based thinking (in line with ISO 9001:2015) at

enterprise level by supporting faster and better decision making at shop-floor. Link the 5

intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-

REPAIR and Z-MANAGE). Implement the designed Z-MANAGE strategy and interface

with MES and/or other high level manufacturing systems in-place.

Provide the inference engine a second layer of autonomous decision support in relation to the

5 Z-Fact0r strategies.

Update the monitoring and inspection conditions and constraints of the ES-DSS.

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Define rights for data sharing and exchanging internally with various enterprise systems and

decision making units, as well as externally with customers and suppliers

2) Types


Digital



Data should be available at XML or JSON format.



be used.

It needs inputs from the Sensor Network (through the Device Manager), the overall model

of Production activities (through Z-Fact0r Repository) and context – aware knowledge

stemming from the Semantic Context Manager (Ontology)


Estimation of the volume of data can be done only by the source.

Standards

3) Ownership


Yes



No

5) Data use



Reaction to Incident detection, re-adaptation of the production processes and notifying

components of Z-Fact0r which has subscribed for these events.


Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access?

As described on GA document.



In a main KM server and also into local terminals when appropriate indications have been

disseminated.



Task: T2.5 / T3.4

WP: WP2 / WP3

WP Leader: CERTH / EPFL

Author: Ziazios Konstantinos (ATLANTIS)

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1) Scope


o Early stage decision support system

o Reverse supply chain system


o Data will be used to model the early stage DSS.

o Models for the supply chain

2) Types


Digital.



o Images

o CSV

o JSON

o Binary


Collected from sensors.


be used.

o From laser scanning

o From user input

o Batch files


Several GBs per day.

Standards

Not known at this stage.

3) Ownership


Only partners of the consortium



No

5) Data use



o Used for modelling

o To visualise processes

o Create visual KPI’s



access?

o Avoiding storage of sensitive data. Stored encrypted always.

o Limited access to confidentiality data.



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o On cloud only for the training / modelling period.

o No production data will store outside the shop floor.



Task: T2.1, T2.2, T3.1

WP: WP2 / WP3

WP Leader: CERTH / EPFL

Author: Toni Ventura (DATAPIXEL)

1) Scope


o High accuracy and high resolution 3D Pointcloud of scanned parts.

o CAD Model by description of the surfaces and geometries of the designed part.

o Deviation map of a 3D representation of surface deviations calculated between a

captured Pointcloud and the reference CAD model.

o MP, definition of the GD&T to be measured in the Pointcloud.


Data generation of WP2 and WP3 will be connected with Z-DETECT activities, in particular

with T2.1, T2.2 and T3.1.

2) Types


Digital



ASCII list of X Y Z, STEP format, STL with annotated deviations and PLY and QIF or

DMO.


Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud analysis software.


be used.

DATAPIXEL 3D Scanner, 3D Pointcloud Analysis software and Industrial partner’s CAD

modelling software.


Typically, Pointcloud have a size between 100 K to 10M points, or 3Mbytes to 300 Mbytes

and deviation maps have a size between 100 K to 1M polygons, or 10Mbytes to 100 MBytes.

Standards

Metadata includes part identification, date and time of data generation, collection,

equipment. Pointcloud, CAD model, deviation map and measurement results are part of the

information associated with the manufactured parts. Also, MP is part of the project

information.

3) Ownership


Only partners of the consortium



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No

5) Data use



Main use will be the automatic detection of defects by two methods: CAD based inspection

and G&T analysis. Also, for automatic measurement of dimensions and geometries based in

nominal values and tolerances and local deviations. This information will be used for defect

detection and process analysis. The data will be shared using the Sensor network manager,

3D Pointcloud Analysis module and stored in the repository for further future analysis



access?




Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud analysis software.



Task: T3.2

WP: WP3

WP Leader: CERTH

Author: Simone Parrotta (HOLONIX)

1) Scope


Within this task data from sensors will be integrated and stored in Z-Fact0r. Retrieved data

will came from sensors and systems from industrial partners.


The task will define and develop the middleware and related tools for the Z-Fact0r sensor

data integrations.

2) Types


Digital data will be stored in Cloud Based DB.



XML, JSON, CSV


Proper term and conditions will be defined later during the project


be used.

Data will be collected from: new sensors placed in the shop floor to support the processes

monitoring; PLC; legacy systems.


Standards

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XML, JSON, CSV

3) Ownership


Z-Fact0r industrial partners will provide confidential data regarding their processes.



5) Data use


They will be used internally for system testing and validation.




access?

Data confidentiality/ sensitive have been already mentioned in the GA. Processes data from

industrial consortium partners should be kept confidential based on internal company

policies.



Data will be stored within Z-Fact0r repository.


At least five years after the project ends.

Task: Task 3.5

WP: WP 3

WP Leader: CERTH

Author: EPFL

1) Scope

State the purpose of the data generation/collection

Data is required for the Z-Fact0r ontology development. Ontology describes semantic

models. The ontology will be used in order to drive the semantic framework. Furthermore,

it will be used for data integration, visualization, inferencing/reasoning.


Context-aware shop-floor analysis and semantic model for the annotation and description

of the knowledge to represent manufacturing system performance. The ontology will

describe the basic entities of the project and model relevant structures of multi-stage

manufacturing processes.

2) Types


Digital



Required input will be data from Z-Fact0r repository (data concerning machines, workers,

actors, activities and processes, production data logs, etc.), e.g. in XML, CSV, etc.

Generated output will be the semantic enrichment of shop-floor data for representation of

processes, actors, alarms, actions, work-pieces/products, etc., e.g. as RDF Triplets

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Data from Z-Fact0r repository (data concerning machines, workers, actors, activities and

processes, production data logs, etc.)


be used.

Data will be stored in a dedicated repository


Less than 1GB

Standards

W3C-OWL, RDF

3) Ownership


Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT)



No

5) Data use



The ontology will be used in order to drive the semantic framework. Furthermore, it will be

used for data integration, visualization, inferencing/reasoning.



access?

Accessible to Z-Fact0r consortium members including the commission services



The Ontology will be uploaded in a server where it will be accessible to Z-Fact0r consortium

members including the commission services



Task: T4.1 - T4.3

WP: 4

WP Leader: Brunel University London

Author: Brunel

1) Scope


The purpose of the data collection and generation is to facilitate the building of the event-

based model, green scheduler using the KPIs, implementation of the scheduler and

extracting cost functions. The raw data will be collected from plants and the output will

provide the metrics for process and control optimisation to minimise defect.


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The fulfilment of the tasks will lead to achieving: An event-base modelling platform and

green scheduler to identify the key parameters that influence and have the largest effect of

creation of defects in the productions process as well as energy consumption and carbon

emissions. They will assist in customisation of the measurement of the KPIs for each

industrial partner in the consortium, and build the framework for implementing control and

optimisation solutions to minimise defect.

2) Types


Mainly digital



Mainly DB driven files that can be converted to CSV, JSON, TXT, HTML, and SML


The agreed T&C of the consortium


be used.

PLC, SCADA, Production Management Systems, Internet, and project Intranet.


Large but not known at this stage.

Standards

Control Area Network, TCP/IP.

3) Ownership


Members of the consortium.



N/A

5) Data use



Members of the consortium, in addition the results of the R&D project will be disseminated

according to the consortium agreement in the form of conference, journal, specialist

magazine/website outlets.



access?

N/A within the sensitive and project oriented data will remain within the boundaries of the

consortium



In local data storages defined and design specifically for the project. Brunel University SERG

Laboratories will have a dedicated storage and computing facility for the project. The data

will then be stored and utilised in accordance with the T&C of the consortium agreement.


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Duration of the project, and potentially five years after the completion of the project for

further research; but it will be with consent of the consortium members in case the data is

to be accessed and used for the purpose of academic exercise (e.g. teaching and publications)

Task: Task 4.2

WP: WP 4

WP Leader: BRUNEL

Author: EPFL

1) Scope


For the validation and verification of the KPI models, i.e. Productivity, Efficiency, Quality

(Customer Satisfaction), Environmental Impact, and Inventory levels.


2) Types


Digital



.docs & formats for discrete event simulation (descriptive) models using off-the-shelf

simulation packages



be used.

Already installed actuators and sensors will be used for monitoring and evaluating the KPIs.


N/A

Standards

3) Ownership




5) Data use



Based on the prediction of the expected results and depending on the measurements

received for the evaluation of the KPIs through the use-cases utilization they will be fine-

tuned in order for afterwards on-line & real-time application of them. Corrective actions will

be considered for the production line based on the results received.



access?

The final deliverable report associated with this task will be public.

All the other data will be Accessible to Z-Fact0r consortium members.



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Duration of the project, and potentially five years after the completion of the project

Task: Task 4.4

WP: WP 4

WP Leader: BRUNEL

Author: EPFL

1) Scope


Design and development of the cost functions for each of the KPIs.


Models will be industry specific and will be defined as monetary loss functions due to loss

of Productivity (OEE, OLE, Resource Utilisation), Efficiency (energy consumption per

produced unit), Quality (process and product quality loss models), Environmental Loss

(emissions of pollutants per produced unit), and Inventory (storage, and work-in-process).

2) Types


Digital



.xls , .doc files



be used.

Financial data from end-users.


Standards

3) Ownership


Z-Fact0r end-users.



No

5) Data use



At a second stage, a validation and verification process of the direct observations and

experiments on the Shop-floor and direct measurement of costs against system state and

contrastively with the simulated ones.



access?



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Task: T7.3, T8.2, T9.2

WP: 7-8-9

WP Leader: INOVA+, CETRI

Author: CONFINDUSTRIA

1) Scope


Regularly, Data generation and Collection is an important requirement aimed to develop

activities/tasks, to allow Analysis, to measure performances and to measure the achievement

of own objectives.


Data generation, and, in particular, data collection will be useful in order to develop market

analysis and Customer Adoption Plan. We also recognize the importance of these options,

generate and collecting data, by thinking to the planned workshops, in which results achieved

will be shared (in respect to the privacy needed).

2) Types


Mainly digital.


generated/collected

.xls files, .ppt files, emails, .doc files


Since we will have to use available info and results from other WP we will respect the

required IP protection, confidentiality of data.


be used.

Data will be collected from website, available DB and we will use the only not confidential

results/information that we could share in order to develop our tasks (Roadmapping,

Customer Adoption plan and DCE).


Unknown

Standards

3) Ownership




We will use existing data coming from web, available surveys or other WPs.

5) Data use



For their definitions and objective, our tasks and results will be useful for the all project

partners and some of them for some potential customers, since our tasks imply Partners and

customers sharing.

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access?

No confidentiality.



Z fact0r Shared digital folders.



Task: 8.1-8.5 & 9.1-9.6

WP:8,9

WP Leader: CETRI

Author: Dr Souzanna Sofou (CETRI)

1) Scope


i) Website: project dissemination and product innovation delivery.

ii) Innovation Management Strategy: Form the IM strategy for the ultimate use and

dissemination of project Results.

iii) Innovation Management Roadmap: Design and implement WPs 8 and 9.

iv) DMP Questionnaire: Manage Research Data, MetaData, before and after project

duration.

v) Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]: as explained in the

vi) W.

vii) Publications: dissemination and communication activities.

viii) Z-Fact0r leaflet: communication activity for project wider acceptance.

ix) Z-Fact0r poster: communication activity for project wider acceptance.


As explained above

2) Types


Digital:

i) Website

ii) Innovation Management Strategy

iii) Innovation Management Roadmap

iv) DMP Questionnaire

v) Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]

vi) Publications (hard copies may also be sent for journal & conference publications)

Hard Copies:

vii) Z-Fact0r leaflet

viii) Z-Fact0r poster



i) Website developed by wordpress

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ii) Innovation Management Strategy .ppt and .pdf file

iii) Innovation Management Roadmap: .xls file

iv) DMP Questionnaire: .doc file

v) Deliverables: [D.8.1-D.8.5], [D9.2-D9.5] doc files, .pdf files website

vi) Publications .doc files, .pdf files

vii) Z-Fact0r leaflet .cdr file, .ppt file, .pdf file

viii) Z-Fact0r poster .cdr file, .ppt file, .pdf file


i), iii), Data taken also from the GA.

iv) Data collected from participants.

v) Data generated during project duration, data from other deliverables will be used.

vi) Research Data generated.

vii) Data taken also from the GA.


be used.

Not applicable for WP8 and WP9.


For digital files: less than 100Mb.

For Hard Copies:

Z-Fact0r leaflet: print on both sides A4 size,

Z-Fact0r poster: print on one side, according to conference restrictions.

Standards

3) Ownership

According to the ownership model.


According to the ownership model.



Data from other WP´s might be used in dissemination and communication files.

5) Data use



All WP8 and 9 data will be useful to the consortium for the ultimate use and dissemination

of project results.



access?

Website Public

Innovation Management Strategy

Private, the DEM has created the file, only the project partners have access

Innovation Management Roadmap:

Private, the DEM has created the file, only the project partners have access

DMP Questionnaire: Private, Only the project partners have access

Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]:

D8.1, D8.2, D8.3, D8.5, D9.2, D9.3, D9.5: Confidential D8.4, D9.1, D9.4: Public

Publications Public, dissemination rules apply

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Z-Fact0r leaflet Public, dissemination rules apply

Z-Fact0r poster Public, dissemination rules apply



All digital files will be stored in FREEDCAMP All hard copy files will be stored by the DEM

as well as all consortium parties.



2.2.5.3 Dataset per WP

Data information of the partners has been used to define the complete RDI that will be generated

in each WP;

WP1 User requirements, specifications, use case analysis WP leader: SIR

Objective: Development of the architecture of the Z-Fact0r system and the definition and

description of the main components. A complete description of the modules included in the

detailed view in order to point out the responsibilities of each module and their interactions with

the global System Architecture. Qualitative and quantitative data generated and collected are aimed

at defining both the user and system requirements and use, prepare the bibliographic and data-

based information, design the workflow and UML diagrams and report on Z-Fact0r strategy and

risk analysis to monitor the status of the manufacturing process in real time.

Data description: The data being collected will enable the KPI’s to be monitored and to generate

history for prediction and correction of the process. Digital data and documents will be preserved

in their incoming format, files generated and used will be mainly consisting in MS documents

released using the following formats (.doc, .pptx, .vpp and .xls files, emails, SQL DB programs,

images for visualizing and conceptualizing the use cases will be released as PDF files).

Instrument and tools: Unified Modelling Language, TBC for either Pointclouds or micro–

profilometry data. Machine sensors, network infrastructure /middleware (device manager)/ shop-

floor (Z-Fact0r repository for machine processes, part condition and worker’s actions).

WP2 Production-process monitoring, detect life-cycle management

and remanufacturing

WP leader:

EPFL

Objective: Data generation will be needed for formulation of data driven model for the defect

prediction from process inputs, correlated to Z-DETECT Module. Supervise and provide

feedback for all the processes executed in the production line, evaluating performance parameters

and responding to defects, keeping historical data. Send efficiently alarms to initiate actions, filter

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out false alarms, increase confidence levels (through previously acquired knowledge) of early

defect detection and prediction.

Data description: Data generated will be digital and any format data can be accepted (.CSV,

.XLS, etc). However, data output will be in the form of matlab files, (.XLM, .JSON, .MAT, .M,

etc).

Instrument and tools: Inputs from the Sensor Network (through the Device Manager), the

overall model of Production activities (through Z-Fact0r Repository) and context – aware

knowledge stemming from the Semantic Context Manager (Ontology) will be necessary.

Terms & Conditions of data generated: Data are collected from the manufacturing processes

(end users’ collection systems).

WP3 Data management and early stage DSS for inspection and

control WP leader: CERTH

Objective: Data is required for the Z-Fact0r ontology development. Ontology describes semantic

models for the annotation and description of the knowledge to represent manufacturing system

performance. The ontology will be used in order to drive the semantic framework. Furthermore,

it will be used for data integration, visualization, inferencing/reasoning. Data from sensors will be

integrated and stored in Z-Fact0r. Retrieved data will came from sensors and systems from


Data description: Digital data will be stored in Cloud Based DB and required input will be data

from Z-Fact0r repository (data concerning machines, workers, actors, activities and processes,

production data logs, etc.). Generated output will be the semantic enrichment of shop-floor data

for representation of processes, actors, alarms, actions, work-pieces/products, etc., e.g. RDF

Triplets, .CSV, .XML, .JSON.

Instrument and tools: Data will be collected from new sensors placed in the shop floor to

support the processes monitoring; PLC; legacy systems.

Data re-use: The ontology will be re-used in order to drive the semantic framework. Furthermore,

it will be used for data integration, visualization, inferencing/reasoning.

WP4 System modelling for fast forward cost functions WP leader: BRUNEL

Objective: The purpose of the data collection and generation is to facilitate the building, validation

and verification of the KPI models, implementation of the scheduler and extracting cost functions.

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The raw data will be collected from plants and the output will provide the metrics for process and

control optimisation to minimise defect.

Data description: Data generate will be mainly digital and DB driven files can be converted to

CSV, XLS, TXT, HTML and XML.

Instrument and tools: Data and instruments that will be used are PLC, SCADA, Production

Management Systems, Internet, and project Intranet. Already installed actuators, sensors and

financial data from end-users will be used for monitoring and evaluating the KPIs.

Data re-use: Data reutilization will be fine-tuned in order for afterwards on-line & real-time

application of them and corrective actions will be considered for the production line based on the

results received.

WP5 Integration & Testing Validation WP leader: ATLANTIS

Objective: Diverse set of technologies will be developed and all s/w and h/w components and

platforms will be integrated throughout a predefined integration methodology. The technology

validation plan is to be defined and executed while applying corrective design and re-

implementation on all detected errors. Furthermore, the methodology validation throughout the

demonstration in relevant environments will be used as well as the evaluation data and feedback

that are going to be collected, analyzed and documented.

Data description: Data generated will be made available from the Z-Fact0r components that will

be integrated into the complete system. Multiple formats will be available, however, all compatible

to commonly agreed standards, most probably Business to Manufacturing Markup Language

(B2MML) in XML form. Moreover, from the evaluation part of the WP data will come out in

XLS format.

Instrument and tools: Data will be provided by the Z-Fact0r components that form the 5

strategies. For this WP, primary, non-analysed data are not considered, rather than the results of

their analysis by the Z-Fact0r tools and components. Data related to end user evaluation will be

most probably collected using online forms and questionnaires, that will allow transfer into XLS

files.

Data re-use: The collected data will be evaluated and considered in order to reach a better

understanding of the processes and activities evolved in the shop-floors. Combined with the input

from the technical validation plan this will lead to fine-tuning of the Z-Fact0r components and

the lessons learned could be transformed into actionable knowledge.

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WP6 Demonstration activities WP leader: INTERSEALS

Objective: Automated quality control, with high accuracy level and predictive system for defect

generation based on online continuous monitoring. The data collected will enable to detect

probability or trends that lead to defects that normally result in scrapping the parts.

Data description: Data generate will be digital and hard copies and data format will be .xls for

sensor history data and volumetric measurement and .jpeg images of the defects, and also SQL

formats, emails, etc.

Instrument and tools: Possibly collected by sensors at a bench top apparatus and optical and

physical sensors to be studied. FT-IR (Infrared spectroscopy): for material checking. System

control (CoMo by Kistler) that concentrate the data from the sensor cavities pressure. Injection

moulding machine parameters: achieved by the connection to the PLC of the injection Machine

(the PLC can be Siemens, Omron, Moog). Data from visual and dimensional checking machine.

Data from the worker that work beside the production cell and will communicate with the

software using Augmented Reality.

Data re-use: The existing data are re-used as really useful for making quotations, for process

study, quality control, traceability and claim answer.

WP 7,

8 & 9

Valorization, market replication,

dissemination/ communication/exploitation WP leader: INNOVA, CETRI

Objective Data generation and collection is an important requirement to develop activities/tasks,

allow analysis, measure performances and measure the achievement of WP objectives as: Website

(dissemination and innovation delivery), DMP Questionnaire (Manage Research Data, MetaData,

before, during and after project duration), Innovation Management Strategy (for the ultimate use

and dissemination of results) and Roadmap (planning WP7, design and implement WPs 8 and 9),

Publications (dissemination and communication activities), Z-Fact0r leaflet and poster

(communication activity wider acceptance) and Market analysis and Customer Adoption Plan.

Data description: Data generated will be digital for Website, Innovation Management Strategy,

Innovation Management Roadmap, DMP Questionnaire, Deliverables and Publications (hard

copies may also be sent for journal & conference publications), and hard copies in the case of Z-

Fact0r leaflet and poster. Format of data generated will be .xls, .ppt, .pdf, .doc and .cdr files and

emails.

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Instrument and tools: Data will be collected from website and available DB, with only non-

confidential results/information that could be shared for the development of WP activities (Road-

mapping, Customer Adoption plan and DCE).

In conclusion, data and data management-related challenges under Z-Fact0r are identified and

addressed mainly within WP1 (T1.1, T1.4), WP2 and WP3. As described in the proposal, data to be

used in the project will include: on-line (nearly) real-time and historical data related to (i) the product

(desired specifications; quality inspection results, etc.); (ii) the production equipment and

environment (e.g. temperature, pressure, vibrations, etc.); (iii) manufacturing/ production and

maintenance (e.g. capacity, planning, etc.). Sources of these data will be: existing sensors and

actuators (such as sensors embedded in production machinery, quality inspection equipment, etc.),

as well as new novel sensors and actuators (such as laser scanning, visual and/or IR cameras, non-

contact profilometers, etc.), and enterprise systems. The type of sensors/ actuators and data to be

used will be defined and finalized per Z-Fact0r use case on the basis of the required metrics at

product and workstation level at single manufacturing stage, and also at multiple stage. Additionally,

non-research data collected related to Innovation Management like the IPR registry that includes

the IP strategy per Result are confidential and are only stored in the FREEDCAMP repository and

the website private area, with no access rights for members outside the consortium.

2.2.6 Policies for access, sharing and re-use

Data generated during Z-Fact0r project will be confidential. Ownership and management of

intellectual property and access will be limited to the project consortium partners. For this purpose,

policies for access, sharing, and re-use have been established:

2.2.6.1 Partners Background

Partners have identified their background for the action (data, know-how or information generated

before they acceded to the Agreement), which will be accessible to each other partners to implement

their own tasks (under to legal restrictions or limits previously defined in the CA). The partners

should be able to access, mine, exploit, reproduce and disseminate the data. This should also help

to validate the results presented in scientific publications. The partner´s background, acquired prior

to the starting date of the project, will remain the sole property of the originating partner, provided

that it was presented in the CA.

2.2.6.2 Data Ownership and Access

The full dataset will be confidential and only the members of the consortium will have access on it.

Special consideration will be taken for the project dissemination dataset (e.g. leaflet, brochures,

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posters, etc.) that will be considered as public information. As described in GA, data generated are

expected to be used internally as input by the other WPs. All the partners will have free-access to

the results generated during the project, the information needed for implementing their own tasks

under the action and for exploiting their own results. Also, this information will be available to EU

institutions, bodies, offices or agencies, for developing, implementing or monitoring EU policies,

however such access rights are limited to non-commercial and non-competitive use.

Regarding ORD Pilot, data that will be generated in the OA will be decided during the course of the

project and can include; final peer-reviewed scientific research articles that will be published in the

online repository after the publication, research data including data underlying publications, curated

data and/or raw data and public deliverables of the project (described in GA). If any document or

dataset are decided to become of OA, a special section into the data management portal

(FREEDCAMP) will be created that should provide a description of the item and link to a download

section. Of course, these data will be anonymized, so as not to have any potential ethical issues with

their publication and dissemination.

2.2.6.3 Naming rules

All data files will be saved using a standardized, consistent file naming protocol agreed by the project

partners, which will include relevant metadata to ensure their accessibility. The metadata standard

proposed is the CERIF.

2.2.6.4 Storage Information

Documents of the dataset will be stored at the data management portal (FREEDCAMP) created

and maintained by CERTH/ITI, while links to the portal will exist at the Z-Fact0r website. The

Data Management Portal developed by the project (FREEDCAMP) in the context of the ORD

Pilot, allows the efficient management of the project’s datasets and provides the proper OA of them

for further analysis and reuse.

The dataset will remain at the data management portal for the whole project duration, as well as for

at least 2 years after the end of the project.

Finally, after the end of the project, the portal is going to be accommodated with other portals at

the same server, so as to minimize the needed costs for its maintenance.

2.2.6.5 Data sharing and dissemination

Data will be reused for corrective actions on the deployed strategies and actions will be suggested

based on correlations by the automatic decision support mechanism. Research data results will be

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disseminated according to the CA in the form of conference, articles in a journal, specialist

magazine/website outlets or conference proceedings for dissemination purposes.

All patent applications and all other publications will require prior agreement in respect to content

and the publication media. To this end, each partner should notify the consortium members about

the content and material they wish to publish/disseminate and a 21 days’ evaluation period will be

provided as stated in the CA.

2.2.6.6 IPR management and security

As an innovation action close to the market, Z-Fact0r project covers high-TRL technologies and

aims at developing marketable solutions. The project consortium includes nine industrial partners

from the private sector, in particular, CETRI, ATLANTIS (Technical Management), HOLONIX,

DATAPIXEL, SIR, INOVA, MICROSEMI (demonstrator/end user), INTERSEALS

(demonstrator), and DURIT (demonstrator). Those partners obviously have Intellectual Property

Rights on their technologies and data, on which their economic sustainability is at stake.

Consequently, the Z-Fact0r consortium will protect that data and get approval of concerned partners

before every data publication.

The data management portal will be equipped with authentication mechanisms, so as to handle the

identification of the persons/organizations that download them, as well as the purpose and the use

of the downloaded dataset.

2.2.6.7 Data expire date

Copyright statements of the Z-Fact0r project will protect any written material produced during its

lifetime. As described in GA, the information and data supplied by all project partners and

documents produced during the project will be protected for a period of five years after the project

completion unless there are agreements between the partners.

After the end of the project, the partners should keep for five years the original documents, digital

and digitalized documents, records and other supporting documentation in order to prove the

proper implementation of the action and the costs they declare as eligible.

2.3 Data currently being produced in Z-Fact0r

This version of the DMP does not include the actual metadata about the data being produced in Z-

Fact0r because there is no dataset generated or collected until delivery date of this deliverable (M6).

Further details will be provided in the next updated version.

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3 Data Management related to Zero-defects Manufacturing

The quality and performance data of the Manufacturing enterprises will be considered private and

will only be available after granting permission. On the other hand, the research data about modelling

procedures, KPI validation, event modelling, inspection and real-time quality control, as well as the

system optimization, which will be collected/generated during Z-Fact0r will be distributed freely.

4 Data Management Portal (FREEDCAMP)

The Data Management Portal, a web based portal name as “FREEDCAMP”, is being used within

the Z-Fact0r project for the purposes of the management of the various datasets that will be

produced by the project, as well as, for supporting the exploitation perspectives for each of those

datasets. FREEDCAMP Portal will need to be flexible in terms of the parts of datasets that are made

publicly available. Special attention is going to be given on ensuring that the data made publicly

available violates neither IPR issues related to the project partners, nor the regulations and good

practices around personal data protection.

4.1 FREEDCAMP portal functionalities

The FREEDCAMP Portal is accessed through a web based platform which enables its users to easily

access and effectively manage the various datasets created throughout the development of the

project.

Regarding the user authentication, as well as the respective permissions and access rights, the

following three user categories are foreseen:

- Admin; the Admin has access to all of the datasets and the functionalities offered by the DMP

and is able to determine and adjust the editing/access rights of the registered members and users

(OA area). Finally, the Admin is able to access and extract the analytics, concerning the visitors of

the portal.

- Member; when someone successfully registers to the portal and is given access permission by the

Admin, she/he is then considered as a “registered Member”. All the registered members will have

access to and be able to manage most of the collected datasets.

Knowledge sharing and public documents, apart from the admin and the registered members, as

OA area will be available for users who will not need to register and they will have access to some

specific datasets, as well as to project outcomes.

Figure 1 shows the Login page of the FREEDCAMP portal.

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Figure 1. Login Page of the FREEDCAMP Portal

FREEDCAMP portal will be easily and effectively managed by the members. A variety of graphs,

pie charts etc. is going to be employed for helping members to easily understand and elaborate the

data. In particular, the architecture of the portal presents special interfaces organized to comply the

information.

All tasks and datasets available in the DMP will be accompanied by a short description of the item

(Figure 2 and 3).

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Figure 2. Data access page of the FREEDCAMP portal.

Figure 3. File access of the FREEDCAMP Portal.

Dataset will be structured in three different folders into FREEDCAMP portal; Tasks, Discussion,

Files. Draft documents and deliverables, and other data will be uploaded on specific tasks folders,

and final version documents will be uploaded into the file section of appropriate folder.

In addition, technical and progress meetings will be scheduled in the FREEDCAMP portal calendar

(Figure 4).

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Figure 4. Calendar access of the FREEDCAMP Portal.

4.2 Data Backup: Private Area of the Project Website

As described in Deliverable 9.1, the website private area can be used by all the partners:

i) for storage of files and confidential deliverables

ii) for providing feedback on work in progress

iii) for exchanging information about upcoming events, conferences, etc.

The website Private Area is only accessible by the consortium partners using a username and

password, and will be used as a backup repository to store data that are either confidential or data

that will be made public after a release date that has been identified by the data owner.

4.3 Open Access Section

A special free access section into the data management portal (FREEDCAMP) will be created to

upload the documents, data and datasets and other information that are decided to become of OA.

The description of the item and the link to a download section will be available into this section. Of

course, these data will be anonymized, so as not to have any potential ethical issues with their

publication and dissemination.

5 Future Work

A spreadsheet has been created that will be used throughout the project for the continuous logging

of data and datasets, as well as the related information that has been previously presented. Figure 5

shows the spreadsheet of Z-Fact0r data and datasets.

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Figure 5. Spreadsheet of Z-Fact0r data and datasets.

Additionally, the release date of the datasets that will be available in the open research data pilot will

be defined in this xls.

More specifically, after the IP protection route has been defined for each “result” in the IPR registry

currently being developed, dissemination actions will take place for some of them (for example for

those for which a patent application has been submitted). As soon as a dissemination action is

complete, data can be uploaded in the open research data pilot.

5.1 Roadmap of actions to update the DMP

This deliverable is a dynamic document and will be updated and augmented throughout the whole

project lifecycle with new datasets and results according to the progress of the activities of the Z-

Fact0r project. Also, the DMP will be updated to include possible changes in the consortium

composition and policies over the course of the project.

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For that purpose, the final version of this report will be delivered 6 month before the end of the

project (M36), reflecting on lessons learnt and describing the plans implemented for sustainable

storage and accessibility of the data, even beyond the project’s duration.

6 Conclusions

This report includes the DMP and describes the RDI that will be generated during Z- Factor project

and the challenges and constraints that need to be taken into account for managing it. In addition,

it describes the updated procedures and the infrastructure used in the project to efficiently manage

the produced data, named as FREEDCAMP Portal. The DMP is identified as starting point for the

discussion with the community about the Z-Fact0r data management strategy and reflects the

procedures planned by the work packages at the beginning of the project.

An elaborated questionnaire has been distributed between the consortium partners, asking them

what kind of data they were expecting to produce and collect during the project. From this

information, it has become clear that currently only work packages 1, 2 and 3, are planning to

generate or collect data that can be classified as relevant information according to the definition of

the European Commission. Nonetheless, DMP is not a fixed document and it can be the case that

this situation evolves during the lifespan of the project. Thus, the DMP will be updated and

augmented with new datasets and results twice during project lifetime with the Project Periodic

Reports.

Regarding storage information, documents generated during the project will be stored in

FREEDCAMP Portal which is the document management system of the project. This information,

data and documents produced during the project will be protected for a period of two years after

the project completion, as it is described in GA.

7 Glossary

Participant Information Sheet

The information sheet is an important part of recruiting research participants. It ensures that the

potential participants have sufficient information to make an informed decision about whether to

take part in your research or not

(http://www.kcl.ac.uk/innovation/research/support/ethics/training/infosheet.aspx).

Consent Form

A form signed by a participant to confirm that he or she agrees to participate in the research and is

aware of any risks that might be involved.

Metadata

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Metadata is data that describes other data. Meta is a prefix that in most information technology

usages means "an underlying definition or description." Metadata summarizes basic information

about data, which can make finding and working with particular instances of data easier.

(http://whatis.techtarget.com/definition/metadata) or http://www.data-

archive.ac.uk/media/54776/ukda062-dps-preservationpolicy.pdf

Repository

A digital repository is a mechanism for managing and storing digital content. Repositories can be

subject or institutional in their focus. (http://www.rsp.ac.uk/start/before-youstart/ what-is-a-

repository/)

8 Bibliography

- Guidelines on Data Management in Horizon 2020, Version 2.0, 30 October 2015:

http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oapilot-

guide_en.pdf

- Guidelines on OA to Scientific Publications and Research Data in Horizon 2020, Version

2.0, 30 October 2015:

http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oapilot-

guide_en.pdf

- Webpage of European Commission regarding OA:

http://ec.europa.eu/research/science-society/open_access

http://whatis.techtarget.com/definition/metadata

http://www.rsp.ac.uk/start/before-youstart/

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