4th Technical Meeting - WP4
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Transcript of 4th Technical Meeting - WP4
Project SLOPE1
WP 4 – Multi-sensor model-based quality control of mountain forest production
Technical Meeting 5 Jul 16
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
The goals of this WP are:• to develop an automated and real-time grading (optimization) system for the forest production, in order to improve log/biomass segregation and to help develop a more efficient supply chain of mountain forest products• to design software solutions for continuous update the pre-harvest inventory procedures in the mountain areas • to provide data to refine stand growth and yield models for long-term silvicultural management
Technical Meeting 5 Jul 16
Work Package 4: T4.1 -done
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
Work Package 4: work to be done T4.2
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
the resources planned: 13 M/Mthe resources utilized:PROBLEMS: Delay in access to processor (machine arrived 25 June 2016)SOLUTIONS: conclude the work, collaborate with BOKU and COMPOLAB
Prototype ready: September 2016
draft: October 2014
accepted: July 2015
Work Package 4: work to be done T4.3
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
T4.3: Evaluation of hyperspectral imaging for the determination of log/biomass quality index
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
the resources planned: 17 M/Mthe resources utilized:PROBLEMS: Delay in access to processor (machine arrived 25 June 2016)SOLUTIONS: conclude the work, collaborate with BOKU and COMPOLAB
draft: May 2014
accepted: July 2015
Prototype ready: September 2016
Work Package 4: work to be done T4.4
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
the resources planned: 5.5 M/Mthe resources utilized:PROBLEMS: Delay in access to processor (machine arrived 25 June 2016)SOLUTIONS: conclude the work, collaborate with COMPOLAB
draft: December 2014
accepted: July 2015
Prototype ready: September 2016
Work Package 4: work to be done T4.5
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
T4.5: Evaluation of cutting process (CP) for the determination of log/biomass CP quality index
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
the resources planned: 6.0 M/Mthe resources utilized:PROBLEMS: Delay in access to processor (machine arrived 25 June 2016)SOLUTIONS: conclude the work, collaborate with COMPOLAB
draft: January 2014
accepted: July 2015
Prototype ready: September 2016
Work Package 4: work to be done T4.6
Quality rules & specificationsCNR, TRE:
Develop tool Harvest Simulator TRE:
Develop models of treesGRA, TRE:
Compare models with real dataTRE, GRA, TRE:
Link automatic system with visualTRE,CNR:
Develop 3D quality indexTRE, CNR:
Measurement of standing treesCNR, TRE:
Measurement of felled treesCNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimal protocolCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Measure NIR on standing treesTRE, CNR, FLY:
Measure NIR on felled treesCNR, GRE:
Measure NIR on processor headCNR, COM:
Measure NIR on pale of logsCNR, BOK:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop NIR quality indexCNR, BOK:
Develop provenance NIR modelsCNR, BOK:
Design data base of NIR spectraBOK, CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usabilityCNR:
Calibration transferBOK, CNR:
Develop models for labCNR, BOK:
Imaging standing trees BOK, FLY, TRE:
Imaging fallen trees BOK, GRE:
Imaging on processor headBOK, COM:
Imaging on pale of logsBOK, CNR:
Develop models for in fieldCNR, BOK:
Compare models with lab dataCNR, BOK:
Develop hyperspectral indexCNR, BOK:
Design data base of hyperspectraBOK, CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimal set-up for the hyperspectral camera, illumination, and sample holdingBOK, CNR:
D01.04
Determine optimal set-up for the NIR sensor, illumination, and sample holdingCNR, BOK:
Develop report on using SWCNR:
Develop models for SW qualityCNR:
Test on standing trees CNR, GRE:
Tests on fallen trees CNR, GRE:
Tests on processor headCNR, COM:
Imaging on pale of logsCNR:
Develop SW quality indexCNR:
Define quality thresholdsCNR:
Analyze material dependant factorsCNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimal set-up for the stress wave measurement, including time of flight and free vibrations sensorCNR:
D01.04
Determine quality requirements for high-end assortments
CNR:
Laboratory scale tests for delimbing energy needs
CNR:
Develop CP quality indexCNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimal set-up for the measurement of cutting forces on the processor headCNR:
D01.04Laboratory scale tests for chain saw energy needs
CNR:
Develop models linking CP in delimbing and quality
CNR:
Develop models linking CP in chain sawing and quality
CNR:
Develop report on using CPCNR:
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
Technical Meeting 5 Jul 16
T4.6: Implementation of the log/biomass grading system
Link in-field data with cloud database
CNR:
Compare automatic and visual grading resultsBOK, CNR:
Determine threshold valuesCNR:
Develop grading expert systemCNR:
Develop algorithm for data fusionCNR, COM, TRE:
In field visual quality assessment CNR, BOK:
Develop data base for prices of woody commodities
CNR, BOK:
Reliability studiesBOK:
Economic advantage studiesBOK, CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identify grading rules for standard and niche productsCNR:
Prepare state-of-the-art report on grading rulesCNR:
the resources planned: 8.0 M/Mthe resources utilized:PROBLEMS: Sum of delays related to other tasksSOLUTIONS: collaborative work
draft: October 2014
accepted: July 2015
Prototype ready: October 2016(second demonstration)
fulfillment of the project work plan:related deliverables (M25)
WP4 M17
task deliverable title type of
deliverable
lead participant
due date foreseen or actual delivery date comment
T4.1D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted
D4.7 estimation of log/biomass quality by external tree shape analysis software tool TRE 31.05.2015 18.12.2015 accepted
T4.2D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 March 2016 accepted
T4.3D4.4 establisghing hyperspectral imaging measurement protocol report BOK 30.11.2014 05.05.2015 accepted
D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.10.2015 April 2016
T4.4D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 May 2016
T4.5D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 April 2016
T4.6D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted
D4.12 implementatio and callibration of prediction models for log/biomass quality classes software tool CNR 31.06.2016 June 2016
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Planning actions for all activities and deliverables to be executed in M31-36:
Finalize + close: D04.9, D04.10, D04.11, D04,12Deliver + finalize + close: -Initiate + deliver: -
Missing deliverables are prototypes!!!Tune sensors installed on the processorImplement user interface (automatic data acquisition)Finalize data/signal post-processingImplement logic for quality indexes
Technical Meeting 5 Jul 16
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
Significant delay related to DoW amendment + deliveries:• the core machine (processor head) has been accessible for software implementation one week ago• series of in-field real data will be acquired on the system configuration during ongoing demonstrations• algorithms will be tuned off-line on the base of data set• the necessary hardware modifications (to be revealed during the demo) will be performed before September 2016• the dedicated experimental campaign (“Christmas trees”) will be performed in September 2016 (to be confirmed) for the final validation of all sub-systems
Technical Meeting 5 Jul 16
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Thank you! – Grazie!
Technical Meeting 5 Jul 16
Multi-sensor model-based quality control of
mountain forest production
Technical Meeting 5/July/16
T.4.3 – Evaluation of hyperspectral imaging (HI) for the determination of log/biomass “HI quality index”
Trento, July 5th, 2016
Zitek Andreas1, Jakub Sandak2, Anna Sandak2, Barbara Hinterstoisser11 University of Natural Resources and Life Sciences, Vienna2 CNR Ivalsa
Overview
Technical Meeting 5/July/16
• Status: Completed (95 %)• Length: 14 Months (From M8 to M21)• Involved Partners
• Leader: BOKU• Participants: CNR, GRAPHITECH, COMPOLAB, FLY, GRE
• Aim: Evaluating the usability of hyperspectral imaging for characterization of bio-resources along the harvesting chain and providing guidelines for proper collection and analysis of data
• Output: • D4.04 Establishing hyperspectral measurement protocol (M13/M15)• D4.09 Estimation of log quality by hyperspectral imaging (M21, Prototype,
software, delivered, model will be updated with data from real situation from processor head)
Technical Meeting 5/July/16
Task 4.3 – Output
D4.04 Establishing hyperspectral measurement protocol (M13/M15)• Methodology, laboratory setup and field transfer
D4.09 Estimation of log quality by hyperspectral imaging (M21)• Labscale investigations (visible range and near infrared hyperspectral cameras)
• Validation by NIR measurements• Application of chemometric approaches for data evaluation and multivariate image
analysis• Identification of most relevant spectral information
• Measurement of same samples with selected sensors for field application• Measurement of selected samples• Model development• Calibration transfer
• Technological implementation on prototype & transfer to (harsh) field conditions• Measurements in real configuration
• Model adoption• Development of the “HI quality index” for quality grading (both: lab and final field)
Review Meeting 5/July/16
D4.03 Hyperspectral measurement protocol – potential HSI application
hyperspectral measurement (wet & rough state at differ-
ent temperatures)
compute wet wood HSI quality index#3
cut pieces for drying, wood moisture determination
chemometric models for wet & rough wood and/or in f ield
chemometric models for wet & rough wood (lab)
collect samples: wood logs
measurement hyperspectral image
measurement of hyperspectral imaging
handheld device
compute HSI quality index#2
compute HSI quality index#5
(optional) measurement hyperspectral
image handheld device
compute HSI quality index#6
tree marking
cutting tree
processor head
pile of logs
expert system & data base
condition rough samples to norm climate (20 °C, 60 %)
hyperspectral measurement (cond. grinded state)
compute the log quality class (optimize cross-cut)
estimated tree quality
forest models
update the forest database
compare results of different temperatures, roughness,
wet and dry states
combine all available char-acteristics of the log
lab
calibration transfer f (MC, surface_quality)
3D tree quality index
NIR quality index
stress wave SW quality index
cutting force CF quality index
compute HSI quality index#1
grind samples
Storage of samples in lab (f rozen -20°C)
measure surface roughness & temp
hyperspectral measurement (cond. rough state)
compute dry wood HSI quality index#4
Technical Meeting 5/July/16
Task 4.3 – Transfer of HSI technologyto processor head
Processor headNIR sensors will be integrated with the processor head (NIR quality index #4). All the sensors will be positioned on a lifting/lowering bar on the head processor near the cutting bar. The cutting bar will be activated in two modes: automatic and manual
3D model of sensor arm
Task 4.3 – Samples with deficits
BOKU education forest at Forchtenstein(Rosalia), Burgenland
25 samples of spruce (Picea abies) withdifferent defects (ø 15 - 45 cm), March 2015
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Task 4.3 – 25 samples (spruce, Picea abies) with defects
resin pockets
eccentric pith + compression wood + rot eccentric pith + rot + knot
shakes, checks, splitsknots
Task 4.3 Model development
Collection oftraining
samples withdifferent deficits
Measurementswith NIR and HSI
Laboratory equipment
Detection ofmost significant
wavelengthregions for
deficitsFirst models, lab
equipment
Measurements withNIR and HSI with
sensors that will beon Processor Head
MicroNIRHamamatsu
Model development and exportwith PLS model exporter
Models can be directly used fordata from scanning bar and theLabview software installed on Compactrio incl. preprocessing
and statistical methodsModels sensor arm equipment
WorkflowLab (scientific basis, calibration transfer)
Calibration & fieldtransfer
Technical Meeting 5 Jul 16
D4.03 Establishing HS measurement protocol – laboratory setups
VIS-NIR HSI system a CNR (spectral range 400 – 1000 nm)
NIR HSI system a BOKU (spectral range 900-1700 nm)
Pushbroom Hyperspectral Imaging Systems at CNR and BOKU
Technical Meeting 5/July/16
NIR used to validate HSI data D4.03 Establishing NIR measurement protocol
Task 4.3 – Results for resin pockets Intensity slabs
Technical Meeting 5/July/16
1190 nm 1377 nm
Task 4.3 – First results training & classification
Training sample - PLS-DA supervised classification
Technical Meeting 5/July/16
Task 4.3 – First results training & classification
Test sample – PLS-DA supervised classification
Class Pred. Membership Class Pred. Probability
Technical Meeting 5/July/16
NIR-Spectrocopic measurementsScientific publication in prep.Principal component analysis for wood and resin (resin pockets)
Scores Loadings
Technical Meeting 5/July/16
Böhm, Zitek et al., in prep, Assessing resin pockets on freshly cut wood logs of spruce by NIR and hyperspectral imaging, European Journal of Wood and Wood Products
NIR-Spectroscopic measurements –BOKU - laboratory
• 14 out of 25 samples wood discs were measured using a FT-NIR with a fibre optic probe at BOKU
Technical Meeting 5/July/16
Technical Meeting 5/July/16
Task 4.3 – Lab measurements of deficits with FT-NIR
Technical Meeting 5/July/16
Task 4.3 S- Selected spectrometers to be used used in field
Task 4.3 Sensor wavelength range comparison
Visible & near infrared range (VNIR)
400 nm
• Visible wavelength range ~ 390 - 700 nm• Near IR wavelength range ~ 700 nm - 2500 μm
2500 nm
FT NIR (lab) 800 – 2400 nm
Hyperspectral (lab) 900 – 1700 nm
MicroNir (sensor)900 – 1700 nm
Hamamatsu C12666MA
340 – 780 nm
Hamamatsu C11708MA
640 – 1050 nm
Range covered by sensors on processor head340 – 1700 nm
Technical Meeting 5 Jul 16
Technical Meeting 5/July/16
Task 4.3 – FT-NIR and MicroNir
Technical Meeting 5/July/16
Task 4.3 – Calibration tarnsfer
Technical Meeting 5/July/16
Task 4.3 – Tests with Hamamatsu sensor prototypes
Sensor prototypes
Evaluation with calibration standard
Technical Meeting 5/July/16
Task 4.3 – Classification accuracy MicroNir, HamatsuVIS & NIR
Technical Meeting 5/July/16
Task 4.3 – Principle of HSI implementation on sensor arm
Spatially arrangeddata yield theimage-like representation(spatial positionof everymeasurementknown)
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Task 4.3 – Labview environment for Index calculation based on model
raw data from scanner 2D interpolation
Technical Meeting 5/July/16
Task 4.3 - Quality indexes -calculations
• PLS models for suitability indexes(0= not suitable, 1 – perfectlysuitable) for different uses(structural, pulp, resonance etc.)
• PLS models for prediction of logsmoisture, density, calorific value etc.
• Classifications models for defectsdetection
• Classification models for qualityclass assignment (A,B,C,D)
Classification as decayHamamatsuNIR
Classification as decayHamamatsuVIS
Task 4.3 Status of the sensor & model development & implementation (D 4.09)
NIR measurements of BOKU samples with MicroNIR
Prototype of sensor arm
HSI measurements of BOKU samples - Hamamatsu
Pototype of LabView software
Focus lenses mounted on Hamatsu sensors
Integration of sensors, soft- & hardware, models Model development & quality index (Prototype D4.09)
Implementation of full system on sensor arm withhard- and software
Ong
oingRe-measurement of samples with final sensor arm system
Model adapationFinal HSI quality model
Technical Meeting 5 Jul 16
Technical Meeting 5/July/16
WP 7 Piloting the SLOPE demonstratorD7.04 Demo report for quality control
The overall reliability of the quality controlsystem established in WP3 and WP4 will beassessed during the pilot case studies (CNR,BOKU).
Classification results of the SLOPE automatedsystem will be compared with segregationresults obtained with the current expert-based classification criteria.
Performance of both criteria will beevaluated and compared. For this purpose,material properties correlated to specific“quality indexes” will be directly measuredfrom samples taken from the different lots.
Final setup of sensors and implementation
Technical Meeting 5/July/16
Thank you for your attention!
The objectives of this task is to optimize testing procedures and prediction models for characterization of wood along the harvesting chain, using acoustic measurements (i.e. stress-wave tests).
A part of the activity will be dedicated to the definition of optimal procedures for the characterization of peculiar high-value assortments, typically produced in mountainous sites, such as resonance wood.
Task Leader: CNRTask Participants: Greifenberg, Compolab
WP4: T 4.4 Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “SW quality index”
Objectives
Technical Meeting 5 Jul 16
WP4: T 4.4 Deliverables
D4.05) Establishing acoustic-based measurement protocol: This deliverable contains a report and protocol for the acoustic-based measurement procedureStarting Date: August 2014 - Delivery Date: December 2014
D4.10) Estimation of log quality by acoustic methods: Numerical procedure for determination of “SW quality index” on the base of optimized acoustic velocity conversion models.Starting Date: January 2015 - Delivery Date: August 2015
Estimated person Month= 6.00
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement protocol: work plan
activity responsible status schedule
Determination of measurement conditions CNR done
Measurement trees in field CNR done
Installation of sensor on processor head COMPOLAB done
Developed of prediction models CNR ongoing October 2016
Implementation of the software in the system CNR ongoing September 2016
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol #1
Time of Flight in SLOPE
l1 l2
t0
t1
t201
110 tt
lv−
=−
02
2120 tt
llv−+
=−
12
221 tt
lv−
=−
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: hardware
Hardware installed by COMPOLAB in collaboration with CNR suitable for stress wave (ToF) measurements include:
instrumented hammer trigger3 axis accelerometer (measurement of t1)1 axis accelerometer (measurement of t2)
+ original system for coupling sensors with wood+ number of sensors/procedures enabling safe measurement
Automatic cycles possible
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: preliminary results
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: preliminary results
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: preliminary results
Time of shift: 0.00365234 sek
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: ToF challenges
•Preliminary tests highlighted great problem with coupling of accelerometers and wood, especially due to bark•Wet wood attenuates a lot stress wave – hardly measurable, especially with ultrasound…•Several properties of log/wood are not known during test (such as MC, density)•What does the value of velocity means? (regarding quality)
Special design of hardware on the processor head
The QI is (may be) computed after processing of log
Experimental campaign is foreseen & self learning system on the base of historic data
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol
Free vibrations
if:f1 = f2 - machine vibrations
f3 <> f1 - free vibrations of log,fundamental frequency
D1
l
D2
time
time
frequency
f2 f3
FFT
f1
frequencyFFT
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: hardware
Hardware installed by COMPOLAB in collaboration with CNR suitable for stress wave (FV) measurements include:
instrumented hammer triggerlaser displacement sensor (measurement of log vibration)1 axis accelerometer (measurement of machine vibrations: compensation)
+ scanning bar+ number of sensors/procedures enabling safe measurement
Automatic cycles possible
Technical Meeting 5 Jul 16
D: 4.5 Establishing acoustic-based measurement
protocol: FV challenges
•Laser displacement sensor’s spot is absorbed by rough surface •Are we measuring free vibrations of log or processor head?•What is the noise of signal?•Several properties of log/wood are not known during test (such as MC, density, diameters, length)•What does the value of frequency means? (regarding quality)
Special sensor with enlarged spot size (Keyence LK-G87)
The QI is (may be) computed after processing of log and related later by RFID identificationExperimental campaign is foreseen & self learning system on the base of historic data
Compensation of LDS results with additional acclerometer
Technical Meeting 5 Jul 16
Conclusions
Sensors were selected
Sensors are installed on the processor
Intensive work is ongoing
Preliminary (real) results allows further implementation
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexObjectives
The goals of this task are:• to develop a novel automatic system for measuring of the cutting resistance of wood processed during harvesting• to use this information for the determination of log/biomass quality index
Technical Meeting 5 Jul 16
Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNRTask Partecipants: Compolab
Starting : October 2014Ending: January 2016Estimated person-month = 4.00 (CNR) + 2.00 (Compolab)
CNR : will coordinate the research necessary, develop the knowledge base linking process and wood properties, recommend the proper sensor, develop software tools for computation of the CP quality index
Compolab: will provide expertise in regard to sensor selection and integration with the processor head + extensive testing of the prototype
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality index
Deliverables
D.4.06 Establishing cutting power measurement protocolReport: This deliverable will contain a report and recommended protocol for collection of data chainsaw and delimbing cutting process.
Delivery Date: January 2015 (M.13) DONE
D.4.11 Estimation of log quality by cutting power analysisPrototype: Numerical procedure for determination of “CP quality index” on the base of cutting processes monitoring
Delivery Date: January 2016 (M.25)
Technical Meeting 5 Jul 16
working time of the cutting tools (knifes and chain): estimation of the tool wear and correction of the cutting forces
position of the saw bar while cross-cutting: monitoring of the cutting progress correction factors related to the determination of the cutting forces and material
characteristics
log diameter (combined with position of the saw bar): determination of the cutting length at each moment of the cross-cutting
position of the main hydraulic actuator while cutting-out branches: monitoring of the de-limbing progress determination/mapping of the detailed knot position
Task 4.5: cutting process quality indexother sources of information
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexworking plan
activity responsible status (end of task)
Assemble sensors and controllers in lab CNR Done
Design solutions for sensors placement COMPOLAB Done
Installation of sensor on processor head COMPOLAB Done
Testing of sensors in the shop COMPOLAB ongoing
Implementation of the software for QI CNR ongoing
Final adjustments + callibrations CNR + COM September
Processor ready for 2nd pilot: October 2016
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexcross-cutting with the chain saw
Hydraulic flow (l/min)
Oil pressure (MPa)
Oil temperature (°C)
Position of the saw (mm)+Total working time of tool (min)Log diameter (mm)
time of one sawing stroke/cycle
cutting resistance log diameter quality Index
“easy” “small” “low” (0,2)
“easy” “small” “very low” (0,0)
“difficult” “small” “very high” (1,0)
“difficult” “big” “high” (0,8)
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexreal data from the log cross-cutting on the ARBRO1000
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexreal data from the log cross-cutting on the ARBRO1000
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexde-branching
Load cell#1 (N)
Load cell#2 (N)
Oil pressure (MPa)
Oil temperature (°C)
Position of the feed piston (mm)+Total working time of tool (min)
time of one debranching stroke/cycle
map of knots
CF quality index#2
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexde-branching
time of one debranching stroke/cycle
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexreal data from delimbing on the ARBRO1000
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexreal data from delimbing on the ARBRO1000
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexde-branching
map of knots – displayed for operator
CF quality index#2
Technical Meeting 5 Jul 16
two quality indexes (numbers in the range from 0 to 1) associated to wood/log properties are determined:
CP quality index #1: reflects the estimation of the “wood density” as related to the cutting resistance during cross-cutting of log by chain saw. The quality index #1 value is unique for the whole log.
CP quality index #1 = f(wood moisture content, tool wear, cutting speed, feed speed, log diameter, ellipsoid shape, presence of defects)
CP quality index #2: reflects the “brancheness” of the log along its length and is estimated by means of signals associated with cutting out branches. The quality index #2 is spatially reolved.
CP quality index #2 = f(hydraulic pressure changes along the log length, changes of cutting forces in time, number of AE events or sound pressure level)
Task 4.5: cutting process quality indexalgorithms for data mining
Technical Meeting 5 Jul 16
Task 4.5: cutting process quality indexChallenges
Important delay with prototype developing: the equipment just now ready for testing
How to interpret the complex data?
How reliable will be measurement of cutting forces in forest?
What is an effect of tool wear?
How to link cutting force (wood density) with recent quality sorting rules?
Delimbing or debarkining?
Technical Meeting 5 Jul 16
Conclusions
Sensors were selected
Sensors are installed on the processor
Intensive work is ongoing
Preliminary (real) results allows further implementation
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the log/biomass grading system
Task Leader: CNRTask Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE
Starting : June 2014Ending: July 2016Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (COMPOLAB) + 1.00 (MHG) + 1.00 (BOKU), 0.50 (GRE) + 1.00 (TRE)
CNR: will coordinate the research necessary, develop the software tools (expert systems) and integrate all available information for quality gradingTRE, GRE, COMPOLAB: incorporate material parameters from the multisource data extracted along the harvesting chainGRAPHITECH: integration with the classification rules for commercial assortments, linkage with the database of market prices for woody commoditiesMHG: propagate information about material characteristics along the value chain (tracking) and record/forward this information through the cloud database BOKU: validation of the grading system
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
Objectives
The goals of this task are:• to develop reliable models for predicting the grade (quality class) of the harvested log/biomass.• to provide objective/automatic tools enabling optimization of the resources (proper log for proper use)• to contribute for the harmonization of the current grading practice and classification rules
• provide more (value) wood from less trees
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
Deliverables
D.4.01 Existing grading rules for log/biomassReport: This deliverable will contain a report on existing log/biomass grading criteria and criteria gap analyses
Delivery Date: October 2014 (M.10) DONE
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedurePrototype: This deliverable will contain a report on the validation procedure, and results of the quality class prediction models, and integration in the SLOPE cloud data base
Delivery Date: June 2016 (M.30)
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
The concept (logic)
3D quality index (WP 4.1)
NIR quality index (WP 4.2)
HI quality index (WP 4.3)
SW quality index (WP 4.4)
CP quality index (WP 4.5)
Data from harvester
Other available info
Quality class
Threshold values and variability models of
properties will be defined for the
different end-uses (i.e. wood processing industries, bioenergy
production).
(WP5)
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#1: Quality index concept
Each index can be between:0 – bad, not suitable, low, , …
and1 – good, proper, perfect, appreciated, , …
Computed for: Suitability modeled separately for different destination fields:
resonance wood, structural timber, pulp/paper, chemical conversion…
Presence of various defects, such as: Rotten wood, knottiness, compression wood, eccentric pith…
Compatibility with standard quality classes
For each task of WP4 series of quality indexes will be computed as default
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#1: QI computation in each task
T4.2 (NIR): NIR spectra used for computation of quality indexes (and suitability) on the base of dedicated PLS models + “profile” of QI change along the log diameter
T4.3 (hyperspectral): VIS-NIR spectra used for computation of quality indexes (and suitability) on the base of dedicated PLS models + “map” of QI change on the log’s cross section
T4.4 (SW ToF): the value of stress wave velocity will be compared with statistically significant set of reference samples; high enough velocity corresponds to high value of QI + possibility to measure along the log length
T4.4 (SW FV): the value of natural frequency will be compared with statistically significant set of reference samples (considering also log dimensions); high enough frequency corresponds to high value of QI
T4.5 (CF cross cutting): the value of resistance for cross cutting (considering the log diameter and sharpness of the chain saw) corresponds to the quality; high resistance indicates high wood density
T4.5 (CF delimbing): the absence of branches indicates high value of the QI, the separation of (three) sensors and monitoring of the stoke position allows mapping of the braches position and make the QI spatially resolved
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#2: NIR + HI QI computation
Set of experimental sampleswith characteristics representingpoor quality QI = “0”
Set of experimental sampleswith characteristics representingsuperb quality QI = “1”
PLS models for prediction
validation of models
implementation of modelsfor routine data processing
never ending tuning process
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#3: summary of QI + weights
weight for each quality aspect
rangeconstruct.
woodbiomass
/fuel pulp plywood class A class DT4.2 moisture 0 - 1 0,2 1
density 0 - 1 1 1 1 1 1carbohydrate content 0 - 1 1lignin content 0 - 1 1 1calorific value 0 - 1 1rotten wood progress 0 - 1 -100 1 1 1early/late wood ratio 0 - 1 0,2 1width of sapwood 0 - 1 0,1pith eccentricity 0 - 1 0,5 0,8 1width of bark 0 - 1 0,2 1 1 1presence of reaction wood 0 or 1 1 1 1 1presence of resin 0 or 1 0,2 1 1presence of rot 0 or 1 -100 0,7 1presence of bark 0 or 1 -0,5 0,2 1 1presence of contamination –soil 0 or 1 -0,1 -0,1presence of contamination – oil 0 or 1 1
T4.3 ovalness 0 - 1 1 2 1ratio of knot area 0 - 1 0,2 1knot count 0 - 1 0,2 1
T4.4 velocity 0 - 1 1 0,8 1homogenity velocity 0 - 1 1 1 1density 0 - 1 1 0,8 1elasticity 0 - 1 1 0,3 1suitability for pales 1
T4.5 knotines 0 - 1 0,5 0,6 1knots size 0 - 1 2 0,6 1knot spatial distribution 0 - 1 1 1 1log density 0 - 1 1 1 1 1easy for processing 0 - 1 1 1 1 1
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#4: maths behind
For each log:
∑∑ ⋅
=i
iimarket w
QIwQ
where:Qmarket – log quality for specific use/marketwi – weight of quality indexQIi – quality index assessed by sensor
)( ii wtresholdQI >∀
where:treshold(wi) – minumum value of QIi
AND/OR*
* - depending on application
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
implementation#4: quality map
Technical Meeting 5 Jul 16
Task 4.6: Implementation of the grading system
Challenges1. Implement + test QI routines separately for each sensor technique2. Combine all Quality indexes3. Confront the novel procedure with the expert evaluation4. Convince industries to the novel approach
Technical Meeting 5 Jul 16
Thank you very much