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


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:



Imaging standing trees BOK, FLY, TRE:

Imaging fallen trees BOK, GRE:

Imaging on processor headBOK, COM:

Imaging on pale of logsBOK, CNR:



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:


Work Package 4: work to be done T4.2









T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR













D04.04

D04.09

BOK

BOK


D01.04












D04.05

D04.10

CNR

CNR


D01.04


CNR:


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:



CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR




T4.2: Evaluation of NIRS as a tool for determination of log/biomass quality index

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR


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










T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR













D04.04

D04.09

BOK

BOK


D01.04












D04.05

D04.10

CNR

CNR


D01.04


CNR:


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:



CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR




T4.3: Evaluation of hyperspectral imaging for the determination of log/biomass quality index













D04.04

D04.09

BOK

BOK


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











T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR













D04.04

D04.09

BOK

BOK


D01.04












D04.05

D04.10

CNR

CNR


D01.04


CNR:


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:



CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR




T4.4: Data mining and model integration of log/biomass quality indicators from stress-wave











D04.05

D04.10

CNR

CNR


D01.04


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











T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR













D04.04

D04.09

BOK

BOK


D01.04












D04.05

D04.10

CNR

CNR


D01.04


CNR:


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:



CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR




T4.5: Evaluation of cutting process (CP) for the determination of log/biomass CP quality index


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:


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











T4.1 3D quality

D03.01

D01.04

D04.07

TRE

D04.02

TRE

D01.04













T4.2 NIR quality

D04.03

CNR

D04.08

CNR













D04.04

D04.09

BOK

BOK


D01.04












D04.05

D04.10

CNR

CNR


D01.04


CNR:


CNR:



D04.11

D04.06

CNR

CNR



CNR:


CNR:


CNR:



CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR




T4.6: Implementation of the log/biomass grading system


CNR:







CNR, BOK:




D04.01

CNR

D04.12

CNR



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



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




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




Thank you! – Grazie!


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


• 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)


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


(optional) measurement hyperspectral

image handheld device


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


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


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



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


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


NIR used to validate HSI data D4.03 Establishing NIR measurement protocol

Task 4.3 – Results for resin pockets Intensity slabs


1190 nm 1377 nm

Task 4.3 – First results training & classification

Training sample - PLS-DA supervised classification


Task 4.3 – First results training & classification

Test sample – PLS-DA supervised classification

Class Pred. Membership Class Pred. Probability


NIR-Spectrocopic measurementsScientific publication in prep.Principal component analysis for wood and resin (resin pockets)

Scores Loadings


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



Task 4.3 – Lab measurements of deficits with FT-NIR


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



Task 4.3 – FT-NIR and MicroNir


Task 4.3 – Calibration tarnsfer


Task 4.3 – Tests with Hamamatsu sensor prototypes

Sensor prototypes

Evaluation with calibration standard


Task 4.3 – Classification accuracy MicroNir, HamatsuVIS & NIR


Task 4.3 – Principle of HSI implementation on sensor arm

Spatially arrangeddata yield theimage-like representation(spatial positionof everymeasurementknown)


Task 4.3 – Labview environment for Index calculation based on model

raw data from scanner 2D interpolation


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



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


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


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


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


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−

=−



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



protocol: preliminary results



protocol: preliminary results

Time of shift: 0.00365234 sek



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



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



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



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


Conclusions

Sensors were selected

Sensors are installed on the processor

Intensive work is ongoing

Preliminary (real) results allows further implementation


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


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


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)


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


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


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)


Task 4.5: cutting process quality indexreal data from the log cross-cutting on the ARBRO1000


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



time of one debranching stroke/cycle


Task 4.5: cutting process quality indexreal data from delimbing on the ARBRO1000



map of knots – displayed for operator

CF quality index#2


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


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?


Conclusions

Sensors were selected

Sensors are installed on the processor

Intensive work is ongoing

Preliminary (real) results allows further implementation


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


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



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)



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)



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



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



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



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



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



implementation#4: quality map



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


Thank you very much

4th technical meeting - wp4

Education