slope final conference - sensors for timber grading in forest

This project has received fundingfrom the European Union’sSeventh Framework Programmefor research, technologicaldevelopment and demostrationunder grant agreement no 604129

www.slopeproject.eu

Sensorsfortimbergradinginforest

JakubSandak1,AnnaSandak1,StefanoMarazza2,AndreasZitek3,BarbaraHinterstoisser3

1CNR-IVALSA,2COMPOLAB,3BOKU

SLOPEFinalConference10th November,EIMA2016,Bologna– Notturno Hall


Multi-sensormodel-basedqualitycontrolofmountainforestproduction

ThegoalsofProjectSLOPE- WP4are:• todevelopanautomatedandreal-timegrading(optimization)systemfortheforestproduction,inordertoimprovelog/biomasssegregationandtohelpdevelopamoreefficientsupplychainofmountainforestproducts

• todesignsoftwaresolutionsforcontinuousupdatethepre-harvestinventoryproceduresinthemountainareas

• toprovidedatatorefinestandgrowthandyieldmodelsforlong-termsilvicultural management

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butoverallgoals…

• todevelopreliablemodelsforpredictingthegrade(qualityclass)oftheharvestedlog/biomass.

• toprovideobjective/automatictoolsenablingoptimizationoftheresources(properlogforproperuse)

• tocontributefortheharmonizationofthecurrentgradingpracticeandclassificationrules

• providemore(value)woodfromlesstrees

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QIcomputationineachtask

• T4.2:NIRspectra– NIR:QI+“profile”ofNIRQIchangealongthelogdiameter• T4.3:VIS-NIRhyperspectralimage– HI:QI+“map”ofQIchangeonthelog’scrosssection

• T4.4:stresswavevelocity– SW#1:QI+possibilitytomeasurealongtheloglength

• T4.4:thevalueofnaturalfrequency– SW#2:QI+possibilitytomeasurewholetreequality

• T4.5:resistanceforcrosscutting– CF#1:QI+effectofthelogdiameterandsharpnessofthechainsaw

• T4.5:resistancefordelimbing – CF#2:QI+mapofknotspositionandsize

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Wooddefectsasseenonthecrosssection

• Text• Text

• Text

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resin pockets

eccentricpith+compression wood +rot eccentricpith+rot +knot

shakes,checks,splitsknots


Qualityindexconcept

ForeachtaskofWP4seriesofqualityindexes arecomputedasdefault,whereeachindexcanbebetween:

“0”– bad,notsuitable,low,L,D…

“1”– good,proper,perfect,appreciated,J,C…

• Suitabilitymodelledseparatelyfordifferentdestinationfields:• Resonancewood,structuraltimber,pulp/paper,chemicalconversion…

• Presence ofvariousdefects,suchas:rottenwood,knottiness,compressionwood,eccentricpith…

• Compatibilitywithstandardqualityclasses

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QIdeterminationprinciple

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Set of experimental sampleswith characteristics representingpoor quality QI = “0”

Set of experimental sampleswith characteristics representingsuperb quality QI = “1”

PLSmodelsforprediction

validationofmodels

implementationofmodelsforroutinedataprocessing

“neverending”tuningprocess


Implementationofthegradingsystem

Theconcept(logic)• Text

• Text• Text

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3Dqualityindex(WP4.1)

NIRqualityindex(WP4.2)

HIqualityindex(WP4.3)

SWqualityindex(WP4.4)

CPqualityindex(WP4.5)

Datafromharvester

Otheravailableinfo

Qualityclass

Thresholdvaluesandvariabilitymodelsofpropertiesaredefinedforthedifferentend-

uses(i.e.woodprocessingindustries,bioenergy

production).

ForestInformationSystem


summaryofQI+weights

• Text• Text

• Text

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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


Maths behind

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For each log:

åå ×

=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


Crosscuttingasasourceofqualityinfo

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0

1

2

3

4

5

6

7

8

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

oilflow(V)

measurement number

oilflowsensor

020406080100120140160180200

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

extension(m

m)

measurement number

linearpotentiometer

0

20

40

60

80

100

120

140

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

hydrau

licpressure(ba

r)

measurement number

hydraulicmotorpressureinlet

02468101214161820

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

hydrau

licpressure(ba

r)

measurement number

hydraulicmotorpressureoutlet


CF#1quality index

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Delimbing asasourceofqualityinfo

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0

5

10

15

20

25

30

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

logd

iameterencod

er(cm)

measurement number

diameterencoder

02000400060008000100001200014000160001800020000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

loglengthencoder(mm)

measurement number

lengthencoder(fiveconsequitivelogs)

-1000

1000

3000

5000

7000

9000

11000

13000

15000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

leftload

cell(N)

measurement number

leftloadcell

-1000

1000

3000

5000

7000

9000

11000

13000

15000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

rightloa

dcell(N)

measurement number

rightloadcell

-1000

1000

3000

5000

7000

9000

11000

13000

15000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

strokelo

adpressure(N)

measurement number

strokeloadpressure

0

20

40

60

80

100

120

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

155

162

169

176

183

190

197

204

211

218

225

232

239

246

253

260

267

274

281

288

295

302

309

316

knivespressure(mA)

measurement number

knivespressure


CF#2quality index and"quality map"

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Stresswavepropagationvelocityasasourceofqualityinfo

• Text• Text

• Text

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TimeofFlightinSLOPE

l1 l2

t0

t1

t201

110 tt

lv-

=-

02

2120 tt

llv-+

=-

12

221 tt

lv-

=-


Timeofflight

Timeofshift:0.00365234sek


Logfreevibrationsasasourceofqualityinfo

• Text• Text

• Text

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Freevibrations

if:f1 = f2 - machine vibrations

f3 <> f1 - free vibrations of log,fundamental frequency

D1

l

D2

time

time

frequency

f2 f3

FFT

f1

frequencyFFT


Freevibrations

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Nearinfraredspectroscopyasasourceofqualityinfo

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Visible&near infrared range (VNIR)

400nm

• Visible wavelength range ~ 390 - 700 nm• Near IR wavelength range ~ 700 nm - 2500 µm

2500nm

FTNIR(lab)800– 2400nm

Hyperspectral (lab)900– 1700nm

MicroNir (sensor)900– 1700nm

HamamatsuC12666MA340– 780nm

HamamatsuC11708MA

640– 1050nm

Rangecovered by sensors onprocessor head340– 1700nm


NIR/HIqualityindex

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QIresults – example oflogprofiles

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DefectdetectionwithNIR

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Hyperspectralimagingasasourceofqualityinfo

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1190nm 1377nm


Conclusions

• Sensorsmeasuringwoodqualitydirectlyontheprocessorinforestisapioneerworkrarelyreportedinthescientificliterature.

• Itwasshownthatallinvestigatedmethodologiesprovidecomplementarycharacterizationofresourcesandmaybehighlyusefulforsupportingoflogsgrading.

• TheprototypedevelopedwithinSLOPEprojectisfullyfunctioninginregardofsensing.Theprocessandthesoftwaresolutionsaresuitableforquantificationoftheselectedlogpropertiesinobjectiveandrepetitiveway.

• BothSLOPEprojectin-fielddemonstrationswereprecioustotestthesystemandtoimprovetechnicalsolutionsapplied.However,thereisstillaspaceforfurtherevolutionofthistechnology.

• Yourfeedbackis also extremly important forus…

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Grazie!inmemoriamofDr FedericoPrandi (1974-2016)

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slope final conference - sensors for timber grading in forest

Education