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Industry 4.0: From Idea to Design to Implementation · Controller Captain Supervisor. Industry 4.0:...
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1 Industry 4.0: From Idea to Design to Implementation February 18, 2018; Kelowna, BC, Canada H. Najjaran (UBC), A. Milani (UBC, CRN, MMRI), M. Koerber (German Aerospace Centre) [email protected] ; [email protected] ; [email protected]
Transcript of Industry 4.0: From Idea to Design to Implementation · Controller Captain Supervisor. Industry 4.0:...
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Industry 4.0: From Idea to Design to Implementation
February 18, 2018; Kelowna, BC, Canada
H. Najjaran (UBC), A. Milani (UBC, CRN, MMRI), M. Koerber (German Aerospace Centre)
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Content• Overview of Industry 4.0 and its Components (Najjaran)
• Composites Manufacturing & Industry 4.0 Engineering (Milani)• An ideal case for the proof-of-concept
• Application perspective at DLR (Koerber):• Challenges in aerospace production• Current collaborative project:
• Automation, digitization and optimization of fabric draping process
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Improved productivity
Optimized product quality
Optimized inventory
management
Improved worker
productivity
Transparency for customers
Optimized processes
Supply chain connection
More attractive
factory
Faster time to market
Industry 4.0: higher efficiency by digitization!
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IIOT information Use• Enterprise Recourse Planning (ERP)• Manufacturing Execution System (MES)• Human Machine Interaction (HMI) software• Programmable Logic Controller (PL C)
Industry 4.0: an ecosystem!
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Smart ProductsData-driven Manufacturing
Industry 4.0: a multifaceted transition!
R&D Pedagogy
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NewcomersLevel 1 - BeginnerLevel 0 - Outsider
Learners Level 2 - Intermediate
LeadersLevel 5 –Top performerLevel 4 – ExpertLevel 3 - Experienced
Technologies
DesignIMPULS Industry 4.0 Readiness Assessment Model
(Schumacher et al., Procedia CIRP, 2016)
Industry 4.0: an incremental transition!
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Hands On Skills
Past Present FutureController Captain Supervisor
Industry 4.0: a new pedagogy!
Distinction for engineers will be their ability to design?
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MappingDesign onto Technologies
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Information
Interaction
Intelligence
• Reliable• Relevant• Real time
• Human• Machine• Objects• Environment
• Learning• Decision making
Smart Products, Data Driven Manufacturing?
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Jacquard’s Weaving Loom (1784)
Assembly Lines (1900’s)
Industrial Robots (1970’s)
Adaptive Robots (current)
Machines with Intelligence
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Definition of AI
1943 – Bombe• Massive Calculations• Cypher & Coding
1997 – Deep Blue• Algorithms• Logic• Uncertainty
2016 – AlfaGo• Big data• Deep learning
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Decision Support Inference Engine
A Priori ETPM-KNB
IOTInput 1
Input n
IOT Output/Updates
Sources of Information • Standards, best practices, expert knowledge • Analytical and empirical models• Numerical models and simulation
Model-based or Rule-based Systems
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AI
Learn
Infer
Big Data and Deep Learning
Why Composites Manufacturing as the first proof-of-concept phase
• The current number of organizations in Canada using composites is estimated at 700 with over $10 billion in revenues (Roughly, 2016).
• The next decade will see an explosion in the use of advanced composite reinforcement and matrix options (Markets and Markets Research Ltd, 2016).
© CRN UBC 2017 – Do not reproduce without written permission
The Composites Conundrum!
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• Better performance to weight ratios; and design tailorability
• Lower manufacturing costs for large and/or complex structures
• Better durability, hence lower maintenance costs
• With composites, the distinction between materials producer and user is blurred– It is this feature that can
provide benefits• This is also a double-edged
sword, and brings with it great responsibility and risk
• When things go well, composites are great success stories, and when things go wrong, industries can go bankrupt
Universal goal: Make high quality (defect-free) and cost-effective parts
© CRN UBC 2017 – Do not reproduce without written permission
Manufacture of High Performance Composites
Aerospace Automotive
Hot drape forming (Modin) process Thermo-forming process
Focus on cost & production speedFocus on quality
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© CRN UBC 2017 – Do not reproduce without written permission
Engineering Composites
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Wet Lay-up; manual processes
Sample applications
Fundamental steps in composite processes
Open mouldingExample
Material deposition & moulding
Curing (thermo-chemo-mechanical) Demoulding
© CRN UBC 2017 – Do not reproduce without written permission
Material and Process Inputs (examples) Design Outcomes
Propagation of Variability and Uncertainty in Processing
Fatigue Safe-LifePrepreg
manufacture
Weave manufacture
Resin manufacture
Fibre manufacture
Resin precursor
manufacture
Operator skill
Cure cycle
Cutting/ Lay-up
Tool Prep.
Operator skill
Pressure
Temperature
Out-time
Consumables used
Bagging procedure
QA
Calibration
Operator skill
Design Load
Damage Tolerance
Load Response
An intelligent, decentralized design support system is needed.
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A Network of Uncertainties and Risk!
(Mesogitis et al, 2014)
(Pot
ter,
2009
)
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Successful Scaling is also the key consideration
L.B. Ilcewicz, Composites Part A, 1999
Production scalingSize scaling
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TOOLING DESIGN CHOICES THAT AFFECT THERMAL HISTORY:• substructure (i.e. open / closed)
• tooling material • faceplate thickness thermal mass
airflow
BOUNDARY CONDITIONS(convective heat transfer in
autoclaves and ovens)
SENSTITIVITY TO THERMALHISTORY OUTCOMES
max. exotherm (𝑇𝑇max )max. thermal lag (Δ𝑇𝑇SS )final degree of cure (DOC)
EQUIPMENT
airflow
TOOL (and CONSUMABLES)
substructure / toolside (hbot)tooling material (ρ, Cp, k)faceplate thickness (2Ltool)
PART
laminate thickness (2Lpart)configuration (solid laminate / sandwich )
MATERIAL(and PROCESS)
material (ρ, Cp, k)resin heat of reaction (�̇�𝐻max)cure cycle (�̇�𝑇, 𝑇𝑇)
ratio (part (charge) : tool )TC location (part / proxy; lead / lag)TC configuration (shielded / gauge)
Interconnection between Design and Manufacturing Decisions
bagside (htop)
Cost Commitment through Product Design and Development
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© CRN UBC 2017 – Do not reproduce without written permission
Solution: Industry 4.0
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Design and development of cyber physical systems (CPS), IOT, data analytics and machine learning
Decision Support Systems
Artificial Intelligence under HMI Expert-opinion Simulation
https://omi.osu.edu
The DLR at a glance
Aeronautics Space
Transport Energy
Security
DLR.de • Folie 26 Marian Körber – German Aerospace Center
DLR.de • Folie 27
Composite Lightweight StructuresEvolution of CFRP in Aeronautics
Origin: Airbus
B737-300 B747-400
B787
A300 A310-200
A320 A340-300A340-600
A380
MD80B757 B767 MD90
B777
A400M
A350 XWB
0%
10%
20%
30%
40%
50%
60%
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Jahr des Erstfluges
Fase
rver
bund
ante
il am
Str
uktu
rgew
icht
A380
A350C
FRP
% s
truct
ural
wei
ght
Year of first flight
Marian Körber – German Aerospace Center
DLR.de • Folie 28
Airbus A380 Structural CFRP-Parts (22% of structural weight)
J-Nose
Sektion 19.1
Vertical tail planes
Horizontal tail planes
Section 19
Rear Pressure Bulkhead
RipsWing boxSource: Airbus
Marian Körber – German Aerospace Center
DLR.de • Folie 29
Composite Lightweight StructuresEvolution of CFRP in Aeronautics
Origin: Airbus
B737-300 B747-400
B787
A300 A310-200
A320 A340-300A340-600
A380
MD80B757 B767 MD90
B777
A400M
A350 XWB
0%
10%
20%
30%
40%
50%
60%
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Jahr des Erstfluges
Fase
rver
bund
ante
il am
Str
uktu
rgew
icht
A380
A350C
FRP
% s
truct
ural
wei
ght
Year of first flight
Marian Körber – German Aerospace Center
DLR.de • Folie 30
Challenges in aerospace production
Strongly cost driven
Comparatively production low volume
High variability of specialized parts
Regular design updates
Flexible reaction to production anomalies
Large component size are common
Very high requirements concerning accuracy
Many current production processes dominated by manual labor
Marian Körber – German Aerospace Center
DLR.de • Folie 31
Automated production of rear pressure bulkhead
Rear Pressure Bulkhead
The development setup at ZLP Augsburg
Marian Körber – German Aerospace Center
DLR.de • Folie 32
Modular gripper system
Double curved deformation realized by one spine and 15 rips
Gripper area consists of 127 controlable gripper units
20 gripper surfaces equipped with optical sensors
Sensor are able to detect relative movements, material types and the distance to surfaces
Marian Körber – German Aerospace Center
Optimization of automated draping process
DLR.de • Folie 33 Marian Körber – German Aerospace Center
DLR.de • Folie 34
Behavior of material during draping
Marian Körber - German Aerospace Center
Material behavior:
Two ways to reduce tension:
• Gliding of material on gripper surface• Shearing of textile
In fiber orientation: Gliding
In 45° to fiber orientation: Shearing
Basic idea to realize an optimized draping process
Initial state Goal stateParameter set
Drapingdigital twin
Training of a black box
DLR.de • Folie 35 Marian Körber – German Aerospace Center
Ongoing developments at UBC & DLR
Gliding sensors
FA
Fiber angle Boundary curve
End-Effector
Camera
Fiber angle sensor
DLR.de • Folie 36 Marian Körber – German Aerospace Center
DLR.de • Folie 37
How to transition from a gripper to industry 4.0 gripper
Achieved developments:
• Gripper geometry adapts to goal geometry of cut piece
• Automatic detection of cut piece position and rotation
• Execution of pre-programmed processes
• “Sensing” material movement
Ongoing developments:
• On-demand planning and execution of processes
• Draping optimization based on machine learning methods
• OPC-UA based control system
Marian Körber – German Aerospace Center
ETPM KnowledgebaseEquipment, Tool, Part, Material (process)
Advanced ManufacturingStakeholders (SMEs, OEMs, …)
Cloud Storage &
Cloud Computation
Government Initiatives (e.g. NSERC Joint Canada–Germany 2 +2, Chair
in Design Engineering,NRC IRAP, MITACS, etc)
Research &
Training
Next step: A roadmap for Collaborative Industry 4.0 Design and Implementation
