1st Technical Meeting - WP4

Work Package 4: Multi-sensor model-based quality

control of mountain forest production

Work Package 4: Multi-sensor model-based

quality control of mountain forest production

Before starting…1. The forest in mountains is peculiar, and very different than such

of flat lands!!!2. Trees in mountains are (mostly) BIG…3. Big/old tree may be or superior quality, or “fuel wood”4. Trees from mountains might be of really high value5. We do support “PROPER LOG FOR PROPER USE”6. The quality of wood/log/tree is an issue!!!!!7. The quality of wood is not only external dimensions, taper and

diameter…



Wood might not be perfect…



Wood from mountains might be priceless…



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



Fine-grained timeline (all tasks):

4TRE 4.1CNR 4.2

BOKU 4.3CNR 4.4CNR 4.5CNR 4.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36



Interim delivery stages (with dates):D.4.01 R: Existing grading rules for log/biomass (December 2014)D.4.02 R: On-field survey data for tree characterization (March 2015)D.4.03 R: Establishing NIR measurement protocol (April 2015)D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)D.4.06 R: Establishing cutting power measurement protocol (July 2015)D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)D.4.10 P: Estimation of log quality by acoustic methods (October 2015)D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)D.4.12 P: Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure (July 2016)



Task 2.34.1.on-field forest survey

GPSPC/PAD3D scanner

3D vision

Tasks 3.14.2-4.3Mark treeConfirm route of cable crane

GPSPC/PADRFID TAGRFID reader

Tasks 3.24.4

Tree felling

Database

NIR QIH QI

RFID reader

RFID TAG(if cross cut)

Portable NIRHyperspectral

AccellerometersOscilloscope

SW QI

Tasks 3.3

Cable crane

Techno carriageGPRSRFID readerWIFISkyline launcherLoad sensorIntelligent chookersGPSPC/PADData loggerBlack box access

Control systemM/M interface

Tasks 3.44.2-4.3-4.4-4.5-4.6Processorde-brunch, cut to length, measures, mark

Load cell for cutting forceCutting feed sensorFeed force sensorDiameter digital caliperLengthRFID readerRFID TAGPC control comp.GPRS/WIFI

HyperspectralNIR scannerKinect® (or similar 3D vision)Microphone/accellerometer

Data loggerBlack box accessCode Printer

Control systemM/M interfaceID backupDatabase

NIR QI + H QI + SW QI + CF QI

Tasks 3.5

Truck

RFID tags are only used for identifying trees/logs along the supply chain, not to store information.Material parameters from sensors are stored in the database

GPSGPRSRFID antennaBUSCANLoad cell

Logistic Software

ID backup

ID backup

Weight, time

Quality class



Risks and mitigating actions:

Significant delay related to changes in the consortium (lack of the practical expertise of the processor head engineers); technical meetings, new partners/collaborators

Properly define real user expectations; contribution of the development of WP1, discussions with stake holders, foresters, users of forest resources

Technologies provided will not be appreciated by “conservative” forest users; demonstrate financial (and other) SLOPE advantages

Difficulties with integration of some sensors with forest machinery; careful planning, collaboration with SLOPE engineers

Thank you very much

Thank you very much

TreeMetrics

“3D Quality Index”

Quality Index

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7cm7cmPulp

7cmPulpPulp M3?

Large Sawlog M3?

Small Sawlog M3?

• Taper Variation• Straightness• Branching• Rot etc.

The Products: General Values

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7cmPulpPulp = €20 per M3

Large Sawlog = €60 per M3

Small Sawlog = €40 per M3

The Problem - “The Collision of Interests”

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Maximise Value

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Maximise Value: Sawlog Lengths

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3.7mOption 1


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3.7mOption 1


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4.3mOption 2


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4.3mOption 2


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4.9mOption 3


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Large Sawlog M3?

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4.9mOption 3

Harvester Optimisation

Log Quality: Straightness (Sweep), Taper, Branching ,Rot,

Our Offering

Forest Mapper - First In The World – Online Forest Mapping & Analysis - Data Management System

Forest Mapper: Automated net area calculation, stratification and Location for ground sample plots to be collected

Sample Plots

Net Area

Stratification

(Inventory Planning)

Terrestrial Laser Scanning Forest Measurement System(AutoStem Forest)

Automated 3D Forest Measurement System

Trusted and Independent Data

Data Mining, Model Integration: e.g. Online Data, Harvest Planning

Harvest Planning: Cutting Production Scenarios

Forest Warehouse- Online Forest Valuation & Harvest Planning System

Latest Development

• Online Market Place• 15,000 forest owners• Irish Farmers Association

Task 4.2

Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass

quality index in mountain forestsTask leader: Anna Sandak (CNR)

Task 4.2: Partners involvement

Task Leader: CNRTask Partecipants: KESLA, BOKU, FLY, GRE

CNR: Project leader, •will coordinate all the partecipants of this task•will evaluate the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain•will provide guidelines for proper collection and analysis of NIR spectra •will develop the “NIR quality index”; to be involved in the overall log and biomass quality grading

Boku: will support CNR with laboratory measurement and calibration transfer

Kesla, Greifenberg and Flyby: will support CNR in order to collect NIR spectra at various stages of the harvesting chain

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

The raw information provided here are near infrared spectra, to be later used for the determination of several properties (quality indicators) of the sample

4.2 Objectives

4.2 Deliverables

Kick-off Meeting 8-9/jan/2014

Deliverable D.4.03 Establishing NIR measurement protocolevaluating 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 M16 April 2015

Deliverable D.4.08 Estimation of log/biomass quality by NIRSet of chemometric models for characterization of different “quality indicators” by means of NIR and definition of “NIR quality index” Delivery Date M20 August 2015Estimated person Month= 3.45

4.2 Timing


Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.2D.4.03

D.4.08test sensors avaliable on the market

finalize conceptdesign/adopt to the processor

test electronic systemassemble hardware

collect reference samplesanalyse reference samples

test hardware + softwarecalibrate system

develop algorithm for NIR qualityindexintegrate NIR quality index with quality grading/optymization (T4.6) D.4.12

D.4.03 Establishing NIR measurement protocol D.4.08 Estimation of log/biomass quality by NIR D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

Electromagnetic spectrum


The study of the interactions between electromagnetic radiation (energy, light) and matter

Spectrofotometers

laboratory

in-field

NIR spectra will be collected at various stages of the harvesting chain

measurement procedures will be provided for each field test

In-field tests will be compared to laboratory results

4.2 Activities: Feasibility study and specification of the

measurement protocols for proper NIR data acquisition

the scanning bar #1 with NIR sensor

4.2 sensor position in the intelligent processor head

CRio

NIR spectra (USB)

4.2 control system

• spectra pre-processing, wavelength selection, classification,calibration, validation, external validation (sampling –prediction – verification)

• prediction of the log/biomass intrinsic “quality indicators”(such as moisture content, density, chemical composition,calorific value) (CNR).

• classification models based on the quality indicators will bedeveloped and compared to the classification based on theexpert’s knowledge.

• calibrations transfer between laboratory instruments(already available) and portable ones used in the fieldmeasurements in order to enrich the reliability of theprediction (BOKU).

4.2 Activities: Development and validation of

chemometric models.

Development of “provenance models”. The set of spectra collected from selected samples (of known provenance and silvicultural characteristics) along the supply chain will be also processed in order to verify applicability of NIR spectroscopy to traceability of wood (CNR).

4.2 Additional deliverable

Wood provenance & NIRS

2163 trees of Norway spruce from 75 location

in 14 European countries2163 samples measured

x 5 spectra/sample = 10815 spectra

Wood provenance & NIRS

Thank you very much

WP4: Multi-sensor model-based quality control of mountain forest production

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”

Task leader: Mariapaola Riggio (CNR)

WP4: T 4.4 Role of partners involved


Task Leader: CNRTask Participants: Kesla, Greifenberg

CNR: will coordinate all the participants to this task and in particular will define the testing procedures and develop the prediction models for characterization of wood along the harvesting chain, using acoustic measurements

Greifenberg: will provide expertise and assistance for the collection for in field measurements of acoustic data on the felled/delimbed stems

Kesla: will provide expertise, in field assistance and product components (mainly sensors) to be tested for the harvester head integration, for in-field acoustic measurements on the logs

WP4: T 4.4 Deliverables


D4.05) Establishing acoustic-based measurement protocol: This deliverable will contain 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

WP4: T 4.4 Timing


Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.4D.4.05

D.4.10finalize concept

field testsdesign/adopy to the processor

test electronic systemassemble hardware

test hardware + softwarecallibrate system

develop algorithm for CP Q_indexintegrate CP quality index with quality grading/optimization (T4.6) D.4.12

D.4.05 Establishing acoustic-based measurement protocolD.4.10 Estimation of log quality by acoustic methodsD.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

WP4: T 4.4 A premise


Stress-waves

Parameters

SW velocity or time-of-flight (SW-TOF)

Acoustic impendance

Damping

Resonance frequency

WP4: T 4.4 Objectives


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.

WP4: T 4.4 Objectives


Task 4.4 does not aim at defining a procedure for the estimation of specific properties (e.g. dynamic moduli, etc.) of the harvested material.

The aim of Task 4.4 is to define a procedure for determination of “SW quality index” that will support final grading of logs.“SW quality index” will be used in combination with the other implemented “quality indices” developed from the multisource data extracted along the harvesting chain.

WP4: T 4.4 Interactions


WP4: interaction with all other tasks

tasks 4.1, 4.2, 4.3: Information aboutmaterial characteristics (such as diameter, length, moisture content and density), estimated through the other non-destructive tests implemented in WP4 and propagated along the harvesting chain, will be incorporated into predictionmodels.

task 4.6: “SW quality index” will be used in combination with the other implemented “quality indices” developed from the multisource data extracted along the harvesting chain. SW quality index

Density, MC, …

geometricaldata

TOF, resonancefrequency


WP4: T 4.4 manual measurement of the log mechanical properties

Task 4.4 will start from recent developments of acoustic-based diagnostics for forest resource segregation.

the scanning bar #1 with free vibrations sensor

WP4: T 4.4 sensor position in the intelligent processor head


WP4: T 4.4

For many years, the sawmilling industry has utilized acoustic technology for lumber

assessment and devices such as the in- line commercialized stress-wave grade sorter

METRIGUARD®VISCAN®


WP4: T 4.4

Recently, in New Zealand prototypes have beendeveloped integrating acoustic (resonance) measurement devices with process heads

The stress wave velocity measuring system for determination of the mechanical properties of the log; ultrasound transducer and ultrasound receiver

WP4: T 4.4 sensor position in the intelligent processor head (2)

CRio

SW waveform

4.2 control system

ultrasound excitation

ultrasound response

WP4: T4.4 Activities


Available acoustic measurement procedures willbe tested in the field:

on the delimbed stem: CNR – Greifenbergon the cut logs: CNR – KESLA

Additionally measurements will be taken by operatorsalong the whole supply chain

Acquisition time of measurement, influence of obstacles and factors limiting instrument performance, reliability/quality of recorded signals and overallvalidation of measurement procedures will be providedfor each field test.


WP4: T4.4 Challenges

Cope with the factors that might influence acoustic data:

• tree structure : Anisotropy, local variability, heterogeneity, presence/absence of branches, bark, etc.

• MC dependent on growing season (sap flow variation), time of measurement from the felling time, weather and environmental conditions, etc

• Type of sensors/coupling/acquisition setup

• Embodiment of acoustic instruments on a mechanized harvester head

Provide reliable data to be coupled with acoustic data:

i.e. Density, geometrical data, defects, MC, etc.

Thank you very much

TASK 4.5Evaluation of cutting process (CP) for the

determination of log/biomass “CP quality index”

Work Package 4: Multi-sensor model-based quality control of mountain forest production

Task 4.5: Cutting Process (CP) for the determination of

log/biomass “CP quality index”

Task Leader: CNRTask Partecipants: Kesla

Starting : October 2014Ending: November2015Estimated person-month = 4.00 (CNR) + 2.00 (Kesla)

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

Kesla : 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 indexDeliverables

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: July 2015 (M.19)

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: November 2015 (M.23)

Task 4.5: cutting process quality indexTiming

Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.5D.4.06

D.4.11finalize concept

design/adopr to the processortest electronic system

assemble hardwaretest hardware + software

callibrate systemdevelop algorithm for CP Q_index

integrate CP quality index with quality grading/optymization (T4.6) D.4.12

D.4.06 Establishing cutting power measurement protocolD.4.11 Estimation of log quality by cutting power analysis D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure

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 quality indexPrinciples

The indicators of cutting forces:• energy demand• hydraulic pressure in the saw feed piston • power consumption

will be collected on-line and regressed to the known log characteristics.

http://www.youtube.com/watch?v=M3Pm9B5xXaI (ARBRO)

http://www.youtube.com/watch?v=XzaPvftspg0 (KESLA)

http://www.youtube.com/watch?v=M3Pm9B5xXaI

http://www.youtube.com/watch?v=XzaPvftspg0

Task 4.5: cutting process quality indexDelimbing system

Schematic of the de-branching system; cutting knives and hydraulic actuator

Task 4.5: cutting process quality indexChainsaw

the scanning bar #1 and the chain saw in the working positions

Task 4.5: cutting process quality indexcontrol system

CRio

cutting forcesaw “push” force

feed force

Task 4.5: cutting process quality indexComments

The working principles of the selected processor head (ARBRO 1000) allows direct measurement of the cutting/feed force as related to (just) the cutting-out branches.

The average density and mechanical resistance will be a result of the analysis of the chainsaw cutting process.

Estimation of the “CP-branch indicator” will be computed only in the case of delimbing on the processor head. In this case, it will be correlated to the “3D-branch indicator” determined from the 3D stem model of the original standing tree (T4.1).

The information will be forwarded to the server in real-time and will support final grading of logs.

Task 4.5: cutting process quality indexChallenges

What sensors are appropriate for measuring cutting forces in processor head?

load cell? tensometer? oil pressure? electrical current?

How to 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?

Thank you very much

TASK 4.6Implementation of the log/biomass grading

system

Work Package 4: Multi-sensor model-based quality control of mountain forest production

Task 4.6: Implementation of the log/biomass grading

system

Task Leader: CNRTask Participants: GRAPHITECH, KESLA, MHG, BOKU, GRE, TRE

Starting : June 2014Ending: July 2016Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (Kesla) + 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, KESLA: 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

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: December 2014 (M.12)

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: July 2016 (M.31)


Timing

Implementation of the log/biomass grading system1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 361 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

T4.6D.4.01

D.4.12surveys

literature researchtest quality measuring systems

develop software for integration of quality indexestest software

calibrate systemvalidate the algorithm/system

D.4.01 Existing grading rules for log/biomassD.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure


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


The concept

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)

color cameras for color mapping of log’s sides


Other avaliable info (1)

multisensor system for 3D/color mapping of logs


Other avaliable info (2)


Results

Several grading rules are in use in different regions and/or niche products: a systematic database of these rules will be developed for this purpose.

• The performance• Reliability • Repetability• Flexibility

of the grading system will be carefully validated in order to quantify advantages from both economic and technical points of view.at different stages of the value chain.


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?

NI CompactRio master

Database

NI CompactRio client Wifi (in field)

FRID

wei

ght

fuel

???

Wifi (home)

Wifi (home)

HDor

GPRMS

Black box

CP NIR HI SW

cam

era

kine

ct

Wifi (in field)

Wifi (home)

Wifi (home)

Thank you very much

Education

1st Technical Meeting - WP4