Slope Final Review Meeting - WP4

Review Meeting 1 Feb 17

T.4.3 – Evaluation of hyperspectral imaging (HI) for the determination of log/biomass

“HI quality index”

Brussels, February 1st, 2017

SLOPEIntegrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas

Overview• Status: Completed (100 %)• Length: 14 Months (planned from M8 to M21, finalization M34)• Involved Partners

• Leader: BOKU• Participants: CNR, GRAPHITECH, COMPOLAB, FLY, GRE

• Goal: 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 (planned for M19,

prototype software delivered at M21, final delivery of integrated prototype M34)


Task 4.3 – Definition „Hyperspectral Imaging“

Hyperspectral

> 10 wavelengths per pixel

Imaging

Visual (spectral) 2D representation of surfaces

1377 nm


Task 4.3 – Main challenge

Pushbroom NIR HSI lab system (900-1700 nm)

Transfer of complex & expensive lab technology to the harsh field conditions on processor head

Spatially arranged retrieval of spectral data during harvesting process


D4.03 Hyperspectral measurement protocol – potential HSI application

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Task 4.3 - D4.04 Establishing HSI measurement protocol - workflow

Collection of training samples

with different deficits

Measurements with NIR and HSI

Laboratory equipment

Detection of most

significant wavelength regions for

deficitsFirst models,

lab equipment

Measurements with NIR and HSI with

sensors that will be on Processor Head

MicroNIRHamamatsu

Model development and export with PLS model exporter

Models can be directly used for data from scanning bar and the Labview software installed on Compactrio incl. preprocessing

and statistical methods Models sensor arm equipment

WorkflowLab (scientific basis, initial models)

Field (calibration transfer & implementation)


Task 4.3 – D4.09 Lab measurements of deficits with FT-NIR


900-1700 nm

Task 4.3 - D4.04 Selecting spectrometers to be used used in field


Hamamatsu C11708MA sensor, 640-1050 nmInstallation into mobile measurement equipment

MicroNIR900-1700 nm

Task 4.3 - D4.04 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 C11708MA

640 – 1050 nm

Sensors on processor head640– 1700 nm


Task 4.3 – D4.09 comparison lab measurements FT-NIR and MicroNir


Comparison between lab-based FT-NIR measurements and measurements by the handheld MicroNir-sensor for the field application – measurements well in accordance, very good performance of the MicroNIR sensor.

Anna Sandak, Jakub Sandak, Katharina Böhm, Andreas Zitek and Barbara Hinterstoisser

Task 4.3 – D4.09 Calibration transfer


Task 4.3 – D4.09 Quality Index model based on MicroNIR measurements


95.4% of the variance explained by PLS-DA model reducing the cross-validation error of prediction below 15%.

Task 4.3 – D4.09 Implementation of HSI technology to processor head (T3.4)Idea: NIR & HSI sensors integrated with the processor head. All the sensors positioned on a lifting/lowering bar on the processor head near the cutting bar.

3D model of sensor arm


Task 4.3 – D4.09 Principle of HSI implementation on sensor arm

Spatially arranged data of an sensor array (either MiroNIR or Hamamatsu sensors) yield the image-like representation (spatial position of every measurement known)


Task 4.3 – D4.09 MicroNIR sensor integrated with scanning bar and software


Quality index NIR#1: decayed wood Quality index NIR#2: bark Quality index NIR#3: reaction wood Quality index NIR#4: sap wood Quality index NIR#5: for knot Quality index NIR#6: resin Quality index NIR#7: normal wood

2D reconstruction of heartwood (=normal wood) diameter based on sapwood dimension.

Heartwood

Sapwood Sapwood

Task 4.3 – D4.09 Tests with Hamamatsu sensor prototypes for HSI

Sensor prototypes; major problem was the selection & implemention of adequate lighting.

Evaluation with calibration standard


Task 4.3 – D4.09 Integration of Hamamatsu sensors with optics and lightning

60x Zoom Mini Phone Camera Lens Microscope Magnifier used as focusing optics Spectral response of the Hamamatsu C11708MA

sensor for various sources of light

Light sources tested

Integration of sensors, optics and two complementary light sources with in plastic cover 3D printed• low-cost standard bulb with

focusing lenses• Visible-IR spectroscopic focused

micro lamp T-3/4 (5 V)


Task 4.3 – D4.09 Quality Indices to be collected by Hamamatsu sensor array

• Quality index HI#1: decayed wood • Quality index HI#2: bark • Quality index HI#3: reaction wood • Quality index HI#4: sap wood • Quality index HI#5: knot • Quality index HI#6: resin • Quality index HI#7: normal wood


Task 4.3 – D4.09 Labview environment for 2D hyperspectral image representation

Raw data from scanner 2D interpolation


16 Hamamatsu sensors arranged in a row with lighting for hyperspectral imaging. Due the collection of spatial coordinates along with the spectral data, image-like representations can be created.

Task 4.3 – D4.09 Classification accuracy Hamamatsu NIR


• Hamamatsu NIR sensor produced far less reliable results than the MicroNIR sensor

• Results and experiences favors MicroNIR sensor to be implemented in a form of array on the scanning bar• compact/unified electronics integrated with the

optical elements and efficient illumination • elimination of electrical noises present in the

custom hyperspectral camera. • Unfortunately, due to high costs not applicable

yet.

Task 4.3 - Results• Protocol & concept for applying hyperspectral imaging in the field

(D4.04, D4.09)• Selection & testing of optimal sensors (D4.03, D4.04, 4.09) • Selection optimal illumination for spectroscopic application (D4.09)• Design and integration of the hyperspectral camera design with the

ARBRO1000 processor (D4.09)• Chemo-metric models suitable to determine quality indexes

(D4.09)• Pre-processing routines of the acquired signals (D4.09)• Development of HI quality indexes (D4.09)• Integration of the hyperspectral quality assessment results with the

FIS of the SLOPE project (Task 4.6, D4.12)Review Meeting

1 Feb 17


Task 4.3 – Conclusions/Recommendations

• MicroNIR superior to Hamamatsu – still expensive• In future novel NIR/HI instruments will emerge

• Better, dedicated Visible-IR spectroscopic focused micro lamp T-3/4 (5 V) better (other partly already burnt)

• Use of sensors with integrated light system and optics• Consider temperature of sensors, as this influences sensor

sensitivity & results• Chemometric models based on in-field measurements by the used

sensors for the desired application• Sensor calibration in field before each application• Careful further integration with the rest of the system

(interferences, data acquisition & online combination etc.)

Task 4.3 – Outlook WP 7 - Piloting the SLOPE demonstrator

D7.04 Demo report for quality controlThe overall reliability of the quality control system established in WP3 and WP4 (involving WT 4.3 results) was assessed during the pilot case studies (CNR, BOKU). Classification results of the SLOPE automated system were compared with segregation results obtained with the current expert-based classification criteria. Performance of both criteria were evaluated and compared. For this purpose, material properties correlated to specific “quality indexes” were directly measured from samples taken from the different logs.

Final setup of sensors and implementation


Thank you!


University of Natural Resources and Life Sciences ViennaAndreas Zitek: [email protected]

mailto:[email protected]

TASK 4.4Data mining and model integration

of log/biomass quality indicators from stress-wave (SW)

measurements, for the determination of the

“SW quality index”

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

Task leader: Jakub Sandak - since September 2015 (former: Mariapaola Riggio) (CNR)

WP4: T 4.4 Deliverables submitted

D4.05) Establishing acoustic-based measurement protocol: This deliverable contains a report and protocol for the acoustic-based measurement procedureDelivery 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.Delivery Date: December 2016

Final Review Meeting 1 Feb 17

The objectives of this task was 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

Objectives


expected novel functionality

allowing quality determination of the resources (log) at the early stage of the processing chain, even before the cross cutting of each log to the length

Measurement of the stress wave propagation, very rarely implemented in the modern forest processors, is foreseen as a precious source of information

Two quality indexes were intended to be estimated:• quality index SW#1: relating the mechanical stress wave velocity with the

overall quality of the processed log • quality index SW#2: relating free vibration of the processed tree with its overall

external quality


Specific actions performed:

• develop the concept and technical scheme• select optimal sensors set for the vibrations (stress wave) detection• design integration of additional sensors with the ARBRO1000 processor• modify the machine mechanics according to the project requirements• integrate/install sensors• develop electrical connection with the on-board controllers• calibrate sensors readings• develop pre-processing routines of the acquired signals• integrate the data acquisition procedure with the operator actions• determine SW#1 and SW#2 quality indexes• integrate the quality assessment results with the Forest Information System of

the SLOPE project• validate the reliability of the method in pilot tests


Hardware: instrumented hammer


Hardware: ToF accelerometers


Hardware: FreeVib LDS


Hardware: electronics


Software for control: user interface


Software for control: Labview – code


determination of SW#1: procedure

l1 l2

t0

t1

t2


Mathematics

01

110 tt

lv

02

2120 tt

llv

12

221 tt

lv

0

0.2

0.4

0.6

0.8

1

SW velocityvmin vmax

Qua

lity

inde

x

vmin: is an arbitrary value corresponding to the minimal stress wave propagation velocity corresponding to materials of very poor quality

vmax: is an arbitrary value corresponding to the expected stress wave propagation velocity corresponding to materials of refined quality


Example of results (ToF)

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

dyna

mic

load

cell

acce

lera

tion

(V)

scan time

dynamic load cell

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

acce

lera

tion

(V)

scan time

3axis X

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

acce

lera

tion

(V)

scan time

3axis Y

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

acce

lera

tion

(V)

scan time

3axix Z


Software for ToF determination:


Computation of SW#1

SW#1 = min (SW#10-1 and SW#10-2 and SW#11-2)


determination of SW#2: procedure

D1

l

D2

time

time

frequency

f2 f3

FFT

f1

frequencyFFT


Mathematics

0

0.2

0.4

0.6

0.8

1

FV frequencyfmin fmax

Qua

lity

inde

x

flaser = FFT(laser)

fcompensation = FFT(accelerometer3)

Freq_min: is an arbitrary value corresponding to the minimal frequency of log vibrations matching to materials of very poor quality

Freq_max: is an arbitrary value corresponding to the high frequency of log vibrations, usually matching to materials of refined quality


Software for Fvib determination:


Computation of SW#2

If SW_FFT_laser ≠ SW_FFT_compensation then{If SW_FFT_laser < Freq_min then SW#2 = 0If SW_FFT_laser >= Freq_min and SW_FFT_laser <= Freq_max then SW#2= (SW_FFT_laser - Freq_min)/( Freq_max - Freq_min)If SW_FFT_laser > Freq_max then SW#2 = 1}


Conclusions

• the prototype developed within SLOPE project is fully functioning in regard of sensing the material properties

• the developed software solutions are suitable for quantification of the selected log properties in an objective and repetitive way

• the quality indexes may be successfully applied for the screening of logs before shipping to the final user

• both in-field demonstrations of the SLOPE project were precious to test the system and to improve the applied technical solutions

• there is still a space for further evolution of this technology and further field test are indispensable for final tuning of the assessment procedures


Recommendations• involve the processor head producer in the further development • the stiffness and rigidness of the scanning bar may be improved • to test other sensing techniques, providing additional information

(such as density, moisture, shape, etc.). • to perform additional intensive calibration campaign in order to

improve the statistical significance


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 leader: Jakub Sandak (CNR)


D.4.06 Establishing cutting power measurement protocol (report)Delivery Date: January 2015

D.4.11 Estimation of log quality by cutting power analysis (prototype)Delivery Date: November 2016


• 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 Leader: CNRTask Participants: Greifenberg, Compolab

Task 4.5: cutting process quality index

Objectives


expected novel functionality

capacity to identify the quality of the resources (log) at the early stage of the processing chain

Measurement of the cutting forces, not implemented in any of the commercially available modern forest processors, was foreseen as a precious source of information.

Two quality indexes were intended to be estimated:• quality index CF#1: relating chain saw cutting forces, oil flow velocity, hydraulic

pressure and log diameter with the local quality of the processed log• quality index CF#2: relating to debranching cutting forces, hydraulic pressure of

the stroke and log length with the overall external quality of the processed log


Specific actions performed:

• develop the concept and technical project• select optimal sensors set• design integration of sensors with the ARBRO1000 processor• modify the machine mechanics according to the project• integrate/install sensors• develop electrical connection with the on-board controllers• calibrate sensors readings• develop pre-processing routines of the acquired signals• integrate the data acquisition procedure with the operator actions• determine CF#1 and CF#2 quality indexes• integrate the quality assessment results with the Forest Information System of

the SLOPE project• validate the reliability of the method in pilot tests


Hardware: chain saw


Hardware: hydraulic sensors


Hardware: load cells


Hardware: stroke position


Hardware: electronics


Software for control: user interface


Software for control: Labview – code


determination of CP#1: procedure

Saw_position =

Hydraulic_power = hydraulic_flow * (pressure_inlet – pressure_outlet) [m3/s * N/m2 = W]


Mathematics

0

0.2

0.4

0.6

0.8

1

Cutting powerPmin Pmax

Qua

lity

inde

x

Pmin: is an arbitrary value corresponding to the mechanical resistance of working components and matches to the cutting of air

Pmax: is an arbitrary value corresponding to the highest normalized resistance of the wood for cutting, indicating resources of the superior mechanical properties

CF#1_nominal = average( Hydraulic_power/Cut_length)


Example of results (chain saw)

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

oil fl

ow (

V)

measurement number

oil flow sensor

020406080

100120140160180200

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

exte

nsio

n (m

m)

measurement number

linear potentiometer

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

hydr

aulic

pre

ssur

e (ba

r)

measurement number

hydraulic motor pressure inlet

02468

101214161820

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

hydr

aulic

pre

ssur

e (ba

r)

measurement number

hydraulic motor pressure outlet


Software for CP#1 determination:


Computation of CP#1

CP#1 = min (mean(CPbottom_end), mean(CPupper_end))


determination of CP#2: procedure

force_central = force_stroke – (force_left + force_right)

force_stroke

force_leftforce_right


Mathematics behind

0

0.2

0.4

0.6

0.8

1

delimbing forceforcemin forcemax

Qua

lity

inde

x

force_min: is an arbitrary value corresponding to the friction of the knifes with bark and/or debarking force

force_max: is an arbitrary value corresponding to the highest acceptable knot size


Example of results (delimbing)

02000400060008000

100001200014000160001800020000

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

113

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log l

engt

h en

code

r (m

m)

measurement number

length encoder (five consequitive logs)

-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

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left

load

cell

(N)

measurement number

left load cell

-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

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righ

t loa

d ce

ll (N

)

measurement number

right load cell

-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

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134

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155

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stro

ke lo

ad p

ress

ure (

N)

measurement number

stroke load pressure


Software for CP#2 determination:


Conclusions

• measuring cutting power (cross cutting logs + debranching) may be highly useful for quality grading

• the prototype developed within SLOPE project is a fully functioning system working autonomously in a background of the routine operation

• the software solutions implemented are suitable for quantification of the selected log properties in objective and repetitive way

• the in-field demonstrations conducted during the project were precious to test the system and enabled technical improvements

• …there is still a space for further evolution of this technology


RecommendationsEven if the final stage prototype is highly advanced, there are some important issues to be considered before real-world implementation:• to involve the processor head producer in the further development • to test additional sensing techniques (e.g. Acoustic Emission)• to perform additional intensive calibration campaign • it is important to include the information on the sharpness of the chain

saw (already available within the SLOPE software) with the measured normalized cutting power


TASK 4.6Implementation of the log/biomass

grading system

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

Task leader: Jakub Sandak (CNR)


D.4.01 Establishing cutting power measurement protocol (report)Delivery Date: October 2014

D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure (report + prototypes)Delivery Date: January 2017


• 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

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

Task 4.6: Implementation of the log/biomass grading system

Objectives


The goals of this presentation

• to summarize all the concepts behind the innovative logs quality grading according to the SLOPE project approach

• to present an algorithm for the quality grading and multiple quality indexes fusion

• to define an example of the alternative quality classes based on the selected log properties and real needs of the downstream industries

• to define a methodology for validation of the quality grading system

• to summarize the technical and economic advantages of the developed quality grading system

• to present a technical solution for integration in the SLOPE cloud database


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


Sensors used for the SLOPE quality indexes determination

• NIR#1… NIR#7: near infrared spectroscopy – Deliverable D4.08• HI#1… HI#7: hyperspectral imaging – Deliverable D4.09• SW#1: Time of Flight – Deliverable D4.10• SW#2: Free vibrations – Deliverable D4.10• CP#1: chain saw – Deliverable D4.11• CP#2: delimbing – Deliverable D4.11

Chemical composition

Wood density

Presence of defects

Ubnormal woodMechanical strength

Branches dimentions

shape

moisture

mass


Log quality grading according to SLOPE: accessible information acquired by the intelligent machines


Log quality grading according to SLOPE: 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)


Log quality grading according to SLOPE: Numerical algorithm for fusion of quality indexes

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


Log quality grading according to SLOPE: approach #1: expert system


Log quality grading according to SLOPE: approach #2: self-learning system

Set of logswith characteristics representingpoor quality QI = “0”

Set of logswith characteristics representingsuperb quality QI = “1”

PLS models for prediction

validation of models

implementation of modelsfor routine data processing

never ending tuning process


Validation of the new system

• D7.04 “Demo report for quality control”: a dedicated report presenting confrontation of the visual assessment and corresponding results obtained by the SLOPE system

• only limited number of experimental results was acquired during SLOPE project demos

• several in-field tests on different assortments of trees, including mountain plantations, thinning and clear cuts were originally planed (and approved by the forestry authorities of Trentino region)

• unfortunately, in-field tests were cancelled due to unavailability of the hardware and extended time dedicated for the prototype adjustments


Integration with the SLOPE cloud data base

additional info introduced to the original JSON downloaded from the FIS before tree processing includes:• RFID tag number of the log• actual dimensions of the log

after processing:• length• small end diameter• bottom end diameter

• quality grade according to the SLOPE approach

• tree status changed to “processed”


Technical advantages of the SLOPE grading system #1/3

• The SLOPE project approach is a unique innovation not yet implemented in any commercial system

• The quality grading is fully integrated with all the other components of the system

• assisting operator in critical decision making:• nearly real-time availability of the multiple quality aspects detected

by sensors• visualization of the quality map on the display in the processor

control room• The result of the quality assessment is objective and very little

person dependent



• The output of the system is a (simple) number (0 to 1)• SLOPE sensing techniques offer complementary assessment of the

traditional visual grading• determination of some material properties not accessible till now

with traditional methods• the time necessary for scanning is reasonable, even if no any

optimization of procedures and/or technical solution was applied yet



• The maintenance of the sensors is very limited and therefore related costs are minimal (not any chemicals, dangerous or expensive consumables necessary for running scanners of logs)

• The grading sensors are independent and can be installed singularly or in any combination

• The system is basically adaptable to any type of processor (being more straightforward the installation on stroke processors)


Economic advantages of the SLOPE grading system #1/2

• The traditional visual log grading operation itself is a cost - any automation of the process is contributing to the overall cost reduction

• Even if the scanning of log quality requires an extra processing time, it does not affect (or affects very little) the overall efficiency (triggered/limited by the cable crane capability to transport logs)

• The economic value of the broad information regarding quality grade of logs is very hard to estimate:

• different assessments than visual • appreciated by the downstream industries (???)• more objective grading, but based on unusual material properties

(mechanical strength, modulus of elasticity, chemical composition, etc.)


Economic advantages of the SLOPE grading system #2/2

• Even if the cost of the developed prototype is rather high, it is possible to minimize cost in further developments:

• replacing sophisticated control system with more robust and simple solutions

• replace current sensors with low-cost version • RFID traceability + automatic quality grading + access to the FIS

database is a clear marketing advantage for the SLOPE system users• elimination of the incorrect operator decisions• an early availability of timber quality data allows more efficient

logistics• potential of online and real-time marketing of timber products,

allowing the allocation of the appropriate raw material to the appropriate user


Conclusions• a complete + complex system capable of automatic quality grading of logs directly

on the processor head is a pioneer work, not reported in the scientific literature• SLOPE project solution is useful to support process optimization• The prototype is a fully functioning system:

• monitoring material properties• interface different hardware moduli• integrate all the information available• determine the quality grade• share this information with the FIS

• the quality quantification is performed on a well-defined, objective and repetitive way

• both SLOPE project in-field demonstrations were precious to test the system and to improve technical solutions applied

• unfortunately, not all the originally planned in-field tuning of the system was performed as expected


Recommendations

• to involve the processor head producer • to test additional sensing techniques (e.g. passive/active thermovision,

acoustic emission, novel NIR/HI instruments emerging the market)

- Multi-sensor approach -

• to extensively validate in the field, with the active participation of the downstream industries

• to perform additional intensive calibration campaign(s)


Open Discussion


Grazie!in memoriam of

Dr Federico Prandi (1974 -2016)Dr Manfred Schwanninger

(1963-2013)Dr James Burger (1963-2014)

Slope Final Review Meeting - WP4

Education

Transcript of Slope Final Review Meeting - WP4