3rd Technical Meeting - WP4
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The goals of the project are to develop automated quality control systems for mountain forest production using multi-sensor models. Work Package 4 involves developing various quality indices using technologies like 3D scanning, near infrared spectroscopy, hyperspectral imaging, stress wave measurements, and analysis of cutting power to optimize log and biomass segregation. The resources planned and utilized, as well as any problems and solutions, are monitored for each method.
3rd Technical Meeting - WP4
- 1. Project SLOPE WP 4 – Multi-sensor model-based quality control of mountain forest production
- 2. 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
- 3. Work Package 4: work to be done T4.1 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 4. T4.1: Data mining and model integration of stand quality indicators from on-field survey Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE draft: October 2014 accepted: July 2015 OCtober 2015 the resources planned: 9 M/M the resources utilized: PROBLEMS: Not reported
- 5. Work Package 4: work to be done T4.2 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 6. T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: the resources planned: 13 M/M the resources utilized: PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015, software Dec 2015) SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB draft: Dec 2015 draft: October 2014 accepted: July 2015
- 7. Work Package 4: work to be done T4.3 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 8. T4.3: Evaluation of hyperspectral imaging for the determination of log/biomass quality index Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 the resources planned: 17 M/M the resources utilized: PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015) SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB Jan 2016 draft: May 2014 accepted: July 2015
- 9. Work Package 4: work to be done T4.4 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 10. T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: the resources planned: 5.5 M/M the resources utilized: PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader SOLUTIONS: intensify efforts, change in staff involved draft: Jan 2016 draft: December 2014 accepted: July 2015
- 11. Work Package 4: work to be done T4.5 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 12. 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 qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: the resources planned: 6.0 M/M the resources utilized: PROBLEMS: delay with access to sensors (arrived January 2016) SOLUTIONS: intensify work, close collaboration with COMPOLAB draft: Jan 2016 draft: January 2014 accepted: July 2015
- 13. Work Package 4: work to be done T4.6 Quality rules &specifications CNR,TRE: Develop tool Harvest Simulator TRE: Develop models of trees GRA,TRE: Compare models with real data TRE,GRA, TRE: Link automatic system with visual TRE,CNR: Develop 3D qualityindex TRE, CNR: Measurement of standing trees CNR,TRE: Measurement of felled trees CNR: T4.1 3D quality D03.01 D01.04 D04.07 TRE D04.02 TRE D01.04 Determine optimalprotocol CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Measure NIR on standing trees TRE,CNR, FLY: Measure NIR on felled trees CNR,GRE: Measure NIR on processor head CNR,COM: Measure NIR on pale of logs CNR,BOK: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop NIR quality index CNR,BOK: Develop provenance NIR models CNR,BOK: Design data base of NIR spectra BOK,CNR: T4.2 NIR quality D04.03 CNR D04.08 CNR Determine usability CNR: Calibration transfer BOK,CNR: Develop models for lab CNR,BOK: Imaging standing trees BOK,FLY, TRE: Imaging fallen trees BOK,GRE: Imaging on processor head BOK,COM: Imaging on pale of logs BOK,CNR: Develop models for in field CNR,BOK: Compare models with lab data CNR,BOK: Develop hyperspectral index CNR,BOK: Design data base of hyperspectra BOK,CNR: T4.3 hyperspectral quality D04.04 D04.09 BOK BOK Determine optimalset-up for the hyperspectral camera, illumination, and sample holding BOK,CNR: D01.04 Determine optimalset-up for the NIR sensor, illumination, and sample holding CNR,BOK: Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: Laboratory scale tests for delimbing energy needs CNR: Develop CP qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR:
- 14. T4.6: Implementation of the log/biomass grading system Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR: the resources planned: 8.0 M/M the resources utilized: PROBLEMS: Delay related to other tasks – difficulties with implementation SOLUTIONS: LAB scanner + prototype software developed in lab + algorithms ready 31.06.2016 draft: October 2014 accepted: July 2015
- 15. fulfillment of the project work plan: related deliverables (M25) WP4 M17 task delive rable title type of deliverable lead particip ant due date foreseen or actual delivery date comment T4.1 D4.2 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 Waiting for final approval T4.2 D4.3 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 T4.3 D4.4 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 T4.4 D4.5 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 T4.5 D4.6 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 T4.6 D4.1 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 NO
- 16. Work Package 4: Multi-sensor model-based quality control of mountain forest production Planning actions for all activities and deliverables to be executed in M25-30: Finalize + close: D04.8, D04.9, D04.10, D04.11 Deliver + finalize + close: D04.12 Initiate + deliver: - Assemble sensors + control system Install sensors in the processor head Continue field tests with portable instruments Calibrate system in the lab (“model tree”) Collaborate with WP3 (and others) in hardware development
- 17. Work Package 4: Multi-sensor model-based quality control of mountain forest production Risks and mitigating actions: Significant delay related to DoW amandment: • the purchase and delivery of sensors delayed set-up of the system in the lab (laboratory scanner) as well as on the processor head; intensify efforts for all involved partners, direct collaboration and working group meetings, involve additional staff for developments, testing and implementation Technologies provided will not be appreciated by “conservative” forest users; demonstrate financial (and other) SLOPE advantages Limited reliability of some sensors when implemented on the forest machinery; careful planning, collaboration with SLOPE (+outside) engineers
- 18. Sensors and electronics (WP3 & WP4) in progress MicroNIR Hamamatsu C11708 Hamamatsu C12666 Accelerometers time of flight Mechanical excitator Accelerometers free vibration LDS correction Laser Displacement Sensor AE sensor + amplifier Tensionmeters 1/4 bridge Dynamic load cell Hydraulic pressure sensor Hydraulic flow sensor Absolute encoders Hamamatsu C11351 NI 9234 NI 9223 NI 9235/NI 9236 NI 9220 Port #8 CompactDaq SENSORS Port #7 Port #6 Port #5 Port #4 Port #3 Port #2 Port #1 LAN port #2 Industrial PC LAN port #1 Port #6 Port #5 Video output + USB port #4 USB port #3 USB port #2 USB port #1 NI 9403 (Digital I/O) Custom line scan camera Port #8 CRio (real time?) MACHINE CONTROL Port #7 Port #6 Port #5 Port #4 Port #3 Port #2 Port #1 SEA 9744 (GSM + GPS) Joystic(s) RFID reader Hydraulic actuators ??? ??? ??? ??? LAN port #5 LAN port #4 LAN port #3 Touch screen T4.2+T4.3T4.4T4.5T4.5T4.4WP3 WP3WP3 NI 9220 Temperatures of oil and air
- 19. Work Package 4: Multi-sensor model-based quality control of mountain forest production Thank you! – Grazie!
- 20. 4.2 Deliverables status Deliverable D.4.03 Establishing NIR measurement protocol evaluating the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain, providing guidelines for proper collection and analysis of NIR spectra. Delivery Date M10, October 2014 - accepted Estimated person Month = 5 Deliverable D.4.08 Estimation of log/biomass quality by NIR Set of chemometric models for characterization of different “quality indicators” by means of NIR and definition of “NIR quality index” Delivery Date M21, September 2015 – draft presenting protocol validation uploaded in dropbox, the deliverable are the models currently improved Estimated Man/Month = 8
- 21. Detailed procedure related to measurement of NIR along the whole harvesting scenario Forest modeling NIR quality index #1 will be related directly to the health status, stress status and to the productivity capabilities of the tree(s) foreseen for harvest – images from FLYBY to be analyzed Tree marking Direct measurement of the NIR spectra by means of portable instruments will be performed in parallel to the tree marking operation (NIR quality index #2) – first trials done in December Cutting of tree testing the possibility of collecting sample of wood in a form of the triangular slice being a part of the chock cut-out from the bottom of the log (NIR quality index #3) – first trials done in December (optional) prepare samples #1 measurement of infrared spectra (wet state) prepare samples #2 condition samples chemometric models for wet wood and/or in field chemometric models for dry/conditioned wood (lab) measurement of infrared spectra collect sample #1: chip of axe collect sample #2: core ~30mm deep collect sample #3: chips after drilling core collect sample #4: triangular slices measurement NIR profile or hyperspectral image measurement profile of infrared spectra consider approach: max slope, pith position, WSEN compute NIR quality index#2 compute NIR quality index#3 compute NIR quality index#4 measurement profile of infrared spectra consider approach: pith position, defects compute NIR quality index#5 tree marking cutting tree processor head pile of logs expert system & data base refresh sample surface measurement of infrared spectra (dry state) compute dry wood NIR quality index#6compute the log quality class (optimize cross-cut) estimated tree quality forest models update the forest database compare results of wet and dry woods combine all available char- acteristics of the log lab Calibration transfer f(MC, surface_quality) 3D tree quality index hyperspectral HI quality index stress wave SW quality index cutting force CF quality index compute NIR quality index#1
- 22. Detailed procedure related to measurement of NIR along the whole harvesting scenario Processor head NIR sensors will be integrated with the processor head (NIR quality index #4). The first trials are foreseen for mid January on the lab scanner during measurement of the model tree Pile of logs The cross section of logs stored in piles is easily accessible for direct measurement. Such measurements will be repeated periodically in order to monitor the quality depreciation and to determine the most optimal scanning frequency. The result of measuring NIR spectra of logs stored in piles will be NIR quality index #5 – first trials done on 60 logs Laboratory Samples collected in the forest will be measured instantaneously after arrival in the laboratory (at the wet state and with rough surface) by using the bench equipment (NIR quality index #6). Campaign done by BOKU with FT instrument, recently parallel measurement at IVALSA with MicroNIR
- 23. Current activity Software developmnet and measurement campaign
- 24. First models – defects detection knots compression wood fungi compression wood wood resin pocket knots resin pocket fungi Defects detection with lab equipment (data source: BOKU, PCA models: CNR) Defects detection with MicroNIR ● - knots ▲- stain ▼- wood (model tree) ■ bark ♦ resin.
- 25. Scheduled activity activity responsible status schedule Determination of measurement conditions of MicroNIR CNR On going Measurement discs from BOKU CNR On going Calibration transfer BOKU February/March 2016 Measurement trees in field CNR On going Data base of spectra for QI CNR/BOKU On going Report from in field measurement CNR done Chemometric models in PLS toolbox CNR/BOKU February 2016 Installation of MicroNIR on processor head COMPOLAB March 2016 Implementation of the software in the system CNR May 2016 Report of NIR traceability CNR March 2016 Use existing models for prediction of calorific value CNR February/March 2016
- 26. Project SLOPE WP4: Multi-sensor model-based quality control of mountain forest production T4.3– Evaluation of hyperspectral imaging (HI) for the determination of log/biomass “HI quality index” Cork, January 19th-21st, 2016 Andreas Zitek, Katharina Böhm, Jakub Sandak, Anna Sandak, Barbara Hinterstoisser BOKU & CNR Technical Meeting, Cork 19.01.2016
- 27. Mid-term Review 2/Jul/15 Task 4.3 – Output D4.04 Establishing hyperspectral measurement protocol • Methodology, laboratory setup and field transfer D4.09 Estimation of log quality by hyperspectral imaging • Labscale investigations ((visible)/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 • Development of transfer options to (harsh) field conditions • Development of the “HI quality index” for quality grading • Technological implementation on prototype
- 28. Fulfillment of the project work plan: related deliverables (M25) WP4 task delive rable title type of deliverable lead particip ant due date foreseen or actual delivery date comment T4.1 D4.2 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 Waiting for final approval T4.2 D4.3 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 T4.3 D4.4 Establishing 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 T4.4 D4.5 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 T4.5 D4.6 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 T4.6 D4.1 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 NO PROBLEMS: Delay in access to sensors, that produce the data to develop the model and implement the system (=D 4.09) SOLUTIONS: intensify efforts, working meetings with CNR, sharing and transfer of samples measured at BOKU with NIR and HSI to CNR for MicroNIR and Hamamatsu measurements, meeting in February at BOKU to produce models, implement system and finalize D4.09 in April 2016
- 29. Task 4.3 – Field transfer options Implementation of the hyperspectral imaging in the field: • Hyperspectral imaging using new technologies Optimal accuracy and spatial resolution Rigidity of sensors (not suitable for harsh conditions) Relatively high cost • Mono/multi spectral imaging the log cross-section Optimal spatial resolution Reasonable cost Poor spectral accuracy Challenges with implementation • Several simple spectrometers installed on the scanning bar & measuring the log cross-section Optimal spectral accuracy and sufficient spatial resolution Reasonable cost Difficulties with implementation Mid-term Review 2/Jul/15 T3.4 Intelligent processor head
- 30. Task 4.3 HSI – general setups • Whiskbroom imaging : During whiskbroom imaging the sample is scanned pixel per pixel in the x–y–spatial direction in a sequential manner. • Staring (staredown) imaging: Staring imaging is done by a two-dimensional camera capturing the spectral information in each pixel x-, y-plane at once. • Implementation of the Pushbroom imaging idea: as a line scanning system with multiple sensors acquiring the information for a reduced set of pixels in the line at once – subsequent interpolation planned and possible. Project meeting 19-22/jan/2015 From: BOLDRINI, B., KESSLER, W., REBNER, K. & KESSLER, R. W., 2012. Hyperspectral imaging: a review of best practice, performance and pitfalls for inline and online applications. Journal of Near Infrared Spectroscopy, 20 (5): 438-508.
- 31. Task 4.3 HSI – general setups Software #1: simulation of the NIR sensor results on the scanning bar. It is possible to simulate the timing of scan by changing the integration time. Cycle time of software (including integration of signal and acquisition of data by USB + display on monitor.) = 0.25 s (4 Hz) Project meeting 19-22/jan/2015
- 32. Task 4.3 HSI – general setups Software #2: simulation of the hyperspectral sensor results on the scanning bar (only single wavelength). Image on the left is an input showing all points measured with hyperspectral system. Image on the right is reconstructed image by using simple interpolation (part of the code is shown, used for reconstruction only). Project meeting 19-22/jan/2015
- 33. settings of scanning density – rotation (degree) settings of scanning density – pixels on the scan bar size of the probe / measured area (pixels, ROI) resolution of interpolated image
- 34. LabView code for reconstruction raw data from scanner 2D interpolation
- 35. Δ radius = 10 Δ angle = 2 probe_size = 10 Δ reconstruction = 20
- 36. Δ radius = 50 Δ angle = 2 probe_size = 50 Δ reconstruction = 20
- 37. Δ radius = 50 Δ angle = 0.5 probe_size = 10 Δ reconstruction = 20
- 38. Δ radius = 50 Δ angle = 2 probe_size = 10 Δ reconstruction = 20
- 39. Δ radius = 20 Δ angle = 1 probe_size = 10 Δ reconstruction = 10 ( computation time…)
- 40. Task 4.3 Model development Collection of training samples with different deficits Measurements with NIR and HSI Laboratory equipment Detection of most significant wavelength regions for deficits First models, lab equipment Measurements with NIR and HSI with sensors that will be on Processor Head MicroNIR Hamamatsu Model development and export with PLS model exporter Models can be directly used for data from scanning bar and the Labview software installed on PC incl. preprocessing and statistical methods Models sensor arm equipment Workflow Lab (scientific basis, calibration transfer) Calibration & field transfer
- 41. 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 head 340 – 1700 nm
- 42. Mid-term Review 2/Jul/15 Task 4.3 – 25 samples (spruce, Picea abies) with defects resin pockets eccentric pith + compression wood + rot eccentric pith + rot + knot shakes, checks, splitsknots Measured with NIR and hyperspectral imaging at BOKU, and MicroNIR and Hamamatsu at CNR
- 43. 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 Meeting 19/Jan/2016
- 44. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings • Principal component analysis for wood and resin/ resin pockets wood resin pocket Wavelength 800-1400 nm and 1st derivative + vector normalized resin
- 45. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings • Principal component analysis for wood and fungi woodfungi Wavelength 800-1400 nm and 1st derivative + vector normalized
- 46. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark and fungi bark wood fungi Wavelength 800-1400 nm and 1st derivative + vector normalized
- 47. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood and fungi bark wood fungi compression wood Wavelength 800-1400 nm and 1st derivative + vector normalized
- 48. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood and fungi bark wood fungi compression wood Wavelength 800-1400 nm and 1st derivative + vector normalized
- 49. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood, knot and fungi bark wood fungi compression wood knot Wavelength 800-1400 nm and 1st derivative + vector normalized
- 50. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood, knot and fungi bark wood fungi compression wood knot Wavelength 800-1400 nm and 1st derivative + vector normalized
- 51. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood, knot, fungi and resin/ resin pockets bark wood fungi compression wood knot resin resin pocket Wavelength 800-1400 nm and 1st derivative + vector normalized
- 52. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood, knot, fungi and resin pockets bark wood fungi compression wood knot resin pocket Wavelength 800-1400 nm and 1st derivative + vector normalized
- 53. NIR-Spectroscopic measurements Data evaluation Meeting 19/Jan/2016 Scores Loadings Principal component analysis for wood, bark, compression wood, knot, fungi and resin pockets bark wood fungi compression wood resin pocket Wavelength 800-1400 nm and 1st derivative + vector normalized knot
- 54. Task 4.3 – Hyperspectral imaging of 23 logs – example resin pockets intensity slabs, final explorations ongoing Brussels 3/jul/2015 1190 nm 1377 nm
- 55. 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 Implementation of full system on sensor arm with hard- and software UntilFebruary/March D4.09inApril
- 56. NIR-Spectrocopic measurements Scientific publication in prep. Principal component analysis for wood and resin (resin pockets) Scores Loadings Meeting 19/Jan/2016 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
- 57. Mid-term Review 2/Jul/15 Thank you for your attention!
- 58. 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: CNR Task 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
- 59. WP4:T 4.4 Deliverables D4.05) Establishing acoustic-based measurement protocol: This deliverable contains a report and protocol for the acoustic-based measurement procedure Starting 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
- 60. T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave Develop report on using SW CNR: Develop models for SW quality CNR: Test on standing trees CNR,GRE: Tests on fallen trees CNR,GRE: Tests on processor head CNR,COM: Imaging on pale of logs CNR: Develop SW quality index CNR: Define qualitythresholds CNR: Analyze material dependant factors CNR: T4.4 stress wave quality D04.05 D04.10 CNR CNR Determine optimalset-up for the stress wave measurement, including time of flight and free vibrations sensor CNR: D01.04 Determine quality requirements for high-end assortments CNR: the resources planned: 5.5 M/M the resources utilized: PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader SOLUTIONS: intensify efforts, change in staff involved draft: Jan 2016 draft: December 2014 accepted: July 2015
- 61. D: 4.5 Establishing acoustic-based measurement protocol: hardware Hardware design has been made by COMPOLAB in collaboration with CNR Two approaches for measuring stress waves on the processor are considered: • ToF (time of flight) • FV (free vibrations)
- 62. D: 4.5 Establishing acoustic-based measurement protocol
- 63. D: 4.5 Establishing acoustic-based measurement protocol Time of Flight in SLOPE l1 l2 t0 t1 t2 01 1 10 tt l v − =− 02 21 20 tt ll v − + =− 12 2 21 tt l v − =−
- 64. 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
- 65. 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
- 66. 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 identification Experimental campaign is foreseen & self learning system on the base of historic data Compensation of LDS results with additional acclerometer
- 67. Conclusions Many factors influence SW propagation in wood. Parameters measured with the other NDT methods will be incorporated in the SW prediction models Multiple linear regression analysis will be implemented for the definition of the importance of the different parameters (regression t-values) for the model. The further development of Task 4.4 is based on the implementation of the lab scanner (i.e. purchase of sensors) For the implementation of the methodology in the real case scenario, some practical issues (e.g. coupling-decoupling of sensors, etc.) have to be considered in combination with activity of Task 3.4 Sensors arrived: work will be done… and is ongoing
- 68. Task 4.5: cutting process quality index Objectives 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
- 69. Task 4.5: Cutting Process (CP) for the determination of log/biomass “CP quality index” Task Leader: CNR Task Partecipants: Compolab Starting : October 2014 Ending: January 2016 Estimated 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
- 70. Task 4.5: cutting process quality index Deliverables D.4.06 Establishing cutting power measurement protocol Report: 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 analysis Prototype: Numerical procedure for determination of “CP quality index” on the base of cutting processes monitoring Delivery Date: January 2016 (M.25)
- 71. 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 qualityindex CNR: T4.5 cutting power quality D04.11 D04.06 CNR CNR Determine optimalset-up for the measurement of cutting forces on the processor head CNR: D01.04 Laboratory 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 CP CNR: the resources planned: 6.0 M/M the resources utilized: PROBLEMS: delay with access to sensors (arrived January 2016) SOLUTIONS: intensify work, close collaboration with COMPOLAB 31.01.2016 draft: January 2014 accepted: July 2015
- 72. 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 index other sources of information
- 73. sensor type simplicity reliability informationquality easyinterpretation lowcost suitableforSLOPE laboratorytests suitableforSLOPE in-fieldapplication load cell strain gauge electric multimeter oil pressure oil flow AE microphone Task 4.5: cutting process quality index comparison of sensors
- 74. Task 4.5: cutting process quality index working plan activity responsible status (end of task) Assemble sensors and controllers in lab CNR Ongoing (Feb 2016) Design solutions for sensors placement COMPOLAB Ongoing (Feb 2016) Report from lab measurements CNR Ongoing (Mar 2016) Installation of sensor on processor head COMPOLAB (Mar 2016) Testing of sensors in the shop COMPOLAB (Mar 2016) Implementation of the software for QI CNR (April 2016) Final adjustments + callibrations CNR + COM (May 2016) Processor ready for pilot: June 2016
- 75. hydraulic pressure sensors , hydraulic flow sensor , termometer , linear gauge Task 4.5: cutting process quality index schematic of the log cross-cutting system of the ARBRO1000
- 76. Task 4.5: cutting process quality index cross-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)
- 77. hydraulic pressure sensor , load cell Task 4.5: cutting process quality index schematic of the instrumented de-branching system of the ARBRO1000
- 78. Task 4.5: cutting process quality index de-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
- 79. Task 4.5: cutting process quality index de-branching time of one debranching stroke/cycle
- 80. Task 4.5: cutting process quality index de-branching map of knots – displayed for operator CF quality index#2
- 81. 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 index algorithms for data mining
- 82. Task 4.5: cutting process quality index Challenges Important delay with prototype developing: the equipment just now ready for testing How to physically install sensors on the processor? 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?
- 83. Thank you very much
- 84. Task 4.6: Implementation of the log/biomass grading system Task Leader: CNR Task Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE Starting : June 2014 Ending: July 2016 Estimated 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 grading TRE, GRE, COMPOLAB: incorporate material parameters from the multisource data extracted along the harvesting chain GRAPHITECH: integration with the classification rules for commercial assortments, linkage with the database of market prices for woody commodities MHG: 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
- 85. 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
- 86. Task 4.6: Implementation of the grading system Deliverables D.4.01 Existing grading rules for log/biomass Report: 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 procedure Prototype: 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)
- 87. T4.6: Implementation of the log/biomass grading system Link in-field data with cloud database CNR: Compare automatic and visual grading results BOK,CNR: Determine threshold values CNR: Develop grading expert system CNR: Develop algorithm for data fusion CNR, COM, TRE: In field visual qualityassessment CNR,BOK: Develop data base for prices of woody commodities CNR,BOK: Reliabilitystudies BOK: Economic advantage studies BOK,CNR: T4.6 quality implementation D04.01 CNR D04.12 CNR Identifygrading rules for standard and niche products CNR: Prepare state-of-the-art report on grading rules CNR: the resources planned: 8.0 M/M the resources utilized: PROBLEMS: Delay related to other tasks within WP4 SOLUTIONS: intensify efforts, implement ready theoretical solutions developed up-to-data 31.06.2016 draft: October 2014 accepted: July 2015
- 88. 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)
- 89. Task 4.6: Implementation of the grading system implementation#1: Quality index concept Each index can be between: 0 – bad, not suitable, low, , … and 1 – 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
- 90. Task 4.6: Implementation of the grading system implementation#2: Quality index computation Set of experimental samples with characteristics representing poor quality QI = “0” Set of experimental samples with characteristics representing superb quality QI = “1” PLS models for prediction validation of models implementation of models for routine data processing never ending tuning process
- 91. Task 4.6: Implementation of the grading system implementation#3: summary of QI + weights weight for each quality aspect range construct. wood biomass /fuel pulp plywood class A class D T4.2 moisture 0 - 1 0,2 1 density 0 - 1 1 1 1 1 1 carbohydrate content 0 - 1 1 lignin content 0 - 1 1 1 calorific value 0 - 1 1 rotten wood progress 0 - 1 -100 1 1 1 early/late wood ratio 0 - 1 0,2 1 width of sapwood 0 - 1 0,1 pith eccentricity 0 - 1 0,5 0,8 1 width of bark 0 - 1 0,2 1 1 1 presence of reaction wood 0 or 1 1 1 1 1 presence of resin 0 or 1 0,2 1 1 presence of rot 0 or 1 -100 0,7 1 presence of bark 0 or 1 -0,5 0,2 1 1 presence of contamination –soil 0 or 1 -0,1 -0,1 presence of contamination – oil 0 or 1 1 T4.3 ovalness 0 - 1 1 2 1 ratio of knot area 0 - 1 0,2 1 knot count 0 - 1 0,2 1 T4.4 velocity 0 - 1 1 0,8 1 homogenity velocity 0 - 1 1 1 1 density 0 - 1 1 0,8 1 elasticity 0 - 1 1 0,3 1 suitability for pales 1 T4.5 knotines 0 - 1 0,5 0,6 1 knots size 0 - 1 2 0,6 1 knot spatial distribution 0 - 1 1 1 1 log density 0 - 1 1 1 1 1 easy for processing 0 - 1 1 1 1 1
- 92. Task 4.6: Implementation of the grading system implementation#4: maths behind For each log: ∑ ∑ ⋅ = i ii market w QIw Q where: Qmarket – log quality for specific use/market wi – weight of quality index QIi – quality index assessed by sensor )( ii wtresholdQI >∀ where: treshold(wi) – minumum value of QIi AND/OR* * - depending on application
- 93. Task 4.6: Implementation of the grading system implementation#4: quality map Map of knots Map of quality QIT4.4 QIT4.1 QIT4.2 QIT4.3 QIT4.5
- 94. Task 4.6: Implementation of the grading system The concept (diagram) Measure 3D shape of several trees Measure NIR spectra of tree X in forest Extract 3D shape of tree X Compute 3D quality in- dexes for log X.1 … X.n Measure NIR spectra of tree X on processor Measure NIR spectra of tree X on the pale Compute NIR quality in- dex for tree X Compute NIR quality in- dexes for log X.1 … X.n Compute NIR quality in- dexes for log X.1 … X.n Data base for harvest data Data base for Forest In- formation System Determine quality grade for log X.1 … X.n T4.1 T4.2 Measure hyperspectral image of tree X in forest Measure cross section image of log X.1 … X.n Measure NIR spectra of tree X on the pale Compute HI quality index for tree X Compute HI quality in- dexes for log X.1 … X.n Compute HI quality in- dexes for log X.1 … X.n T4.3 Measure stress waves on tree X in forest Measure stress waves of tree X on processor Measure stress waves of log X.1 …X.n on the pale Compute SW quality in- dex for tree X Compute SW quality in- dexes for log X.1 … X.n Compute SW quality in- dexes for log X.1 … X.n T4.4 Measure delimbing force on log X.1 … X.n Measure cross-cutting force on log X.1 … X.n Compute CF quality in- dexes for tree X Compute CF quality in- dexes for log X.1 … X.n T4.5
- 95. Task 4.6: Implementation of the grading system The concept (diagram)#1 Measure 3D shape of several trees Measure NIR spectra of tree X in forest Extract 3D shape of tree X Compute 3D quality in- dexes for log X.1 … X.n Measure NIR spectra of tree X on processor Measure NIR spectra of tree X on the pale Compute NIR quality in- dex for tree X Compute NIR quality in- dexes for log X.1 … X.n Compute NIR quality in- dexes for log X.1 … X.n Data base for harvest data Determine quality for log X.1 … T4.1 T4.2 Measure hyperspectral image of tree X in forest Measure cross section image of log X.1 … X.n Measure NIR spectra of tree X on the pale Compute HI quality index for tree X Compute HI quality in- dexes for log X.1 … X.n Compute HI quality in- dexes for log X.1 … X.n T4.3
- 96. Task 4.6: Implementation of the grading system The concept (diagram)#2 Measure stress waves on tree X in forest Measure stress waves of tree X on processor Measure stress waves of log X.1 …X.n on the pale Compute SW quality in- dex for tree X Compute SW quality in- dexes for log X.1 … X.n Compute SW quality in- dexes for log X.1 … X.n T4.4 Measure delimbing force on log X.1 … X.n Measure cross-cutting force on log X.1 … X.n Compute CF quality in- dexes for tree X Compute CF quality in- dexes for log X.1 … X.n T4.5
- 97. Task 4.6: Implementation of the grading system The concept (diagram)#3 Compute 3D quality in- dexes for log X.1 … X.n Compute NIR quality in- dex for tree X Compute NIR quality in- dexes for log X.1 … X.n Compute NIR quality in- dexes for log X.1 … X.n Data base for harvest data Data base for Forest In- formation System Determine quality grade for log X.1 … X.n Compute HI quality index for tree X Compute HI quality in- dexes for log X.1 … X.n Compute HI quality in
- 98. Task 4.6: Implementation of the grading system data flow & in-field hardware NI CompactRio master Database NI CompactRio client FRID weight fuel ??? Data storage CP NIR HI SW camera kinect
- 99. Task 4.6: Implementation of the grading system Challenges What sensors set is optimal (provide usable/reliable information)? How to merge various types of indexes/properties? Can the novel system be accepted by “conservative” forest (and wood transformation) industry? How the SLOPE quality grading will be related to established classes? the final answer possible only after demonstrations
- 100. Thank you very much