deliverable d.7.04 demo report for quality control · simple software tools capable of data...

SLOPE - Integrated proceSsing and controL systems fOr sustainable forest Production in mountain arEas – FP7-NMP-2013-SME-7 --604129 WP 7 – Piloting the SLOPE demonstrator Deliverable 7.04 Demo report for quality control

Co-founded by the European Commission of 26

Deliverable D.7.04

Demo report for quality control WP7 – Piloting the SLOPE demonstrator

Revision: Final

Authors: Andreas Zitek, Jakub Sandak

Dissemination level PU (Public)

Contributor(s) Martin Kühmeier (BOKU), Maximilian Kastner (BOKU)

Reviewer(s) Umberto di Staso, Federico Devigili, Daniele Magliocchetti

Editor(s) Andreas Zitek, Jakub Sandak

Partner in charge(s) BOKU (CNR, MHG, GRE, TRE, ITENE)

Due date 31-Dec-16

Submission Date 27-Jan-17



REVISION HISTORY AND STATEMENT OF ORIGINALITY

Revision History

Revision Date Author Organisation Description

1.0 21.12.2016 Andreas Zitek BOKU First draft

2.0 26.01.2017 Andreas Zitek BOKU Second draft

3.0 26.01.2017 Federico Devigili, Umberto Di Staso

GraphiTech First revision

4.0 27.01.2017 Jakub Sandak CNR Second revision

5.0 27.01.2017 Andreas Zitek BOKU Final revision

6.0 27.01.2017 Daniele Magliocchetti GraphiTech Quality Check

Statement of originality

This deliverable contains original unpublished work except where clearly indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation or both.



Table of contents REVISION HISTORY AND STATEMENT OF ORIGINALITY ............................................ 2

Revision History ........................................................................................................ 2

Statement of originality ............................................................................................ 2

Table of contents ...................................................................................................... 3

List of figures ............................................................................................................. 3

List of tables .............................................................................................................. 3

Acronyms .................................................................................................................. 4

1 Introduction .................................................................................................... 5

1.1 Time progress of deliverable development ............................................... 5 1.2 Goal ............................................................................................................ 6

2 Summary of SLOPE quality indicators collected for logs quality control in Italy and Austria ........................................................................................................ 7

2.1 Sover ........................................................................................................... 7 2.2 Annaberg .................................................................................................... 8

3 Results of quality control .............................................................................. 11

4 Interpretation of results ............................................................................... 20

5 Conclusion .................................................................................................... 22

6 APPENDIX ...................................................................................................... 23

6.1 Computation rules for quality indices ...................................................... 23 6.2 Grading rules according to the Austrian Practices for Timber Trading .... 24 6.3 Data collection form based on the grading rules according to the Austrian Practices for Timber Trading ............................................................................... 26

List of figures Figure 1 : Planned scheme of data acquisition. ........................................................ 8

Figure 2: Scheme of the demo and evaluation of the quality control system during the pilot demonstration in Annaberg, Austria. ............................................... 10

List of tables Table 1: Comparison of grading results (visual grading by non-certified experts vs.

SLOPE indicators from intelligent processor head); with comments on the decisive criteria for the visual grading, the decisive criteria for the SLOPE grading are shown in bold, the lowest index values are shown in red. .......... 20

Table 2: Threshold values for the definition of the quality indices calculated based on different sensor data (in detail described in D.4.12); note that only those values are given, where data have been collected during the Annaberg pilot study in Austria; all numbers are defined by experts, the QI threshold is a minimum value of QI defined by expert. ......................................................... 23



Acronyms CP Cutting Process

CF Cutting Force

HI Hyperspectral Image

NIR Near InfraRed

QI Quality Index

rm running meter

SW Stress Wave

ToF Time of Flight

WP Work Package

QI Quality Index



1 Introduction

This report summarizes the results gathered during the two executed pilot field demonstrations of the quality control system conducted within SLOPE project, as part of WP7, Task 7.3 “Trials and validation cycle”. During the two field pilot demonstrations in Italy and Austria the SLOPE framework (data collection, novel harvesting systems, model based quality control, ERP) was applied, tested and validated.

This report aims at an estimation of the overall reliability of the quality control system established in WP3 and WP4. For this purpose, classification results of the SLOPE automated system are compared with sorting results obtained with current expert-based classification criteria. Criteria of both systems are applied to a selected set of logs, and the results are compared and discussed.

A special attention has been given to the relation between the samples material properties characterized by the measured quality indexes and the visual examination of an expert.

1.1 Time progress of deliverable development

As a final outcome of the pilot demonstrations with regards to the sensor-based grading system, the deadline for the Deliverable D.7.04 was M36. However, due to the strong linkage with other activities and deliverables of the project (e.g. Implementation and calibration of prediction models of log/biomass quality classes and report on the validation procedure in WP4 – D.4.12) the deliverable has been delayed and delivered at month 37.

The first data collection for this deliverable using the first working prototype was planned for the SLOPE project demo in Sover (Italy) at M30. Although the system hardware was correctly assembled and integrated with the processor head, only simple software tools capable of data acquisition in manual mode were available at that time. Additionally, the system calibration, planned before the Sover pilot, could not be performed but after the event, where the system was applied for the first time in real working conditions, the intelligent processor and its sensors have been extensively upgraded by adjusting both hardware and software for the second SLOPE pilot demonstration in Annaberg, (Austria).

During the second SLOPE pilot demonstration at M34, the upgraded version of the prototype was successfully transported to the field and applied in harsh field conditions (snow, rain, cold temperatures) to collect data and compare the collected sensor data to the expert grading scheme based on the European norm system (EN 1927-1) and typically used in Austria and. Several functionalities were



already integrated with the machine control system and it was possible to perform preliminary validation tests but without a fully automatized quality grading implemented and only a limited number of trees could be processed. Therefore, the data generated during the demonstration allowed a limited validation of the system’s reliability although data from 8 single logs could be collected for an initial validation of the sensor based grading system related to the classical visual judgment.

The experiences and data gained during the second demo resulted in a further improvement of the SLOPE processor software, especially for the integration of quality indexes and improved algorithms for the near-real-time quality grading of logs. In the future, with improved quality indexes, increased number of processed logs and an improved strategy for in-field calibration of the sensors, the knowledge about the system performance and reliability can be further improved.

1.2 Goal

The main goals of this report are the following:

• To describe the methodological setup of the quality control system at the two pilot demonstrations in Italy and Austria

• To summarize the results of the quality control system gathered during the two pilot demonstrations in Italy and Austria

• Present the data gathered by the automated sensor-based quality control system from 8 individual logs

• Compare and discuss the data by the automated sensor-based quality control system in relation to the visual expert-based grading system of wood logs

• To draw conclusions about the future technical and economic advantages of the developed quality grading system



2 Summary of SLOPE quality indicators collected

for logs quality control in Italy and Austria

The quality indexes gathered by the automated quality control system of SLOPE have been described in detail in D.4.12 “Implementation and calibration of prediction models of log/biomass quality classes and report on the validation procedure” and listed in the tables of D.7.031 “Demo Report for Data Collection I” and D.7.032 “Demo Report for Data Collection II”. In this section the general methodological setup and the related set of quality criteria collected at each pilot demonstrations will be described. However, it has to be mentioned that the demos were not as intensive as expected due to the prototype nature of the equipment, change of plans and delays in development of the prototype.

2.1 Sover

In Sover, Italy (E 11.3296022, N 46.2424933) between 04th and 08th July 2016 for the first time the SLOPE processor head and sensor system was transferred into the field. The sensors were connected to the software for data acquisition, but there was no integration with the control system, so start of data acquisition had to be triggered manually. A showcase about the project in the context of Sover pilot demonstrator can be found on the official project video channel1.

The collection of grading data was planned to follow the scheme described in Figure 1. The focus was the comparison of the automated sensor-based data with the independent quality grading provided by non-certified and certified experts, based on existing grading rules for log/biomass (described in D.4.1 “Existing grading rules for log/biomass”). Finally, the feedback of a saw mill to the quality grading from their point of view should complete the assessment of the reliability of the quality control system.

This comprehensive deployment scheme could not be applied as planned, since due to technical problems with the initial prototype of the processor head no logs could be retrieved. After the Sover pilot in Italy, the processor was modified by Compolab at CNR premises and the sensor–related software was fully integrated with control system although not all developments were finalized until the Annaberg pilot.

1 Video of Sover pilot: https://www.youtube.com/watch?v=N9_Gvsssi5A&feature=youtu.be

https://www.youtube.com/watch?v=N9_Gvsssi5A&feature=youtu.be



Figure 1 : Planned scheme of data acquisition.

2.2 Annaberg

The pilot demonstrator was conducted in Annaberg, Austria (E 13.422370 N 47.495400) between 10th and 14th October 2016. Most of the planned sensor functionalities were correctly operational on the intelligent processor head with the exception of the hyperspectral cameras and the MicroNir sensor (the prototype not assembled on the scanning bar). The functionality of the laser system for measuring the free vibrations was also limited due to failure of the proximity sensor securing the scan bar operation. A showcase highlighting the collection of different sensor parameters in the context of Annaberg pilot demonstrator can be found on the official project video channel2.

Besides these unforeseen complications the following data were retrieved from eight fully processed logs in the field:

• CP#1 QI (chain saw): cutting force data (in field) o CF#1 quality index describes mechanical properties of the timber

assessed as the resistance of the material to the cross cutting by the chain saw. In general, high value of cutting forces is associated with elastic materials and indicates superior quality.

• CF#2 QI (delimbing): debranching data (in field) o CF#2 quality index describes presence of wood defects, especially

knots, assessed during tree delimbing process. Knots are the most frequent wood defects having a tremendous effect on the log value.

• SW#1 QI (ToF): stress wave (time of flight, in field) 2 Video of Annaberg pilot: https://www.youtube.com/watch?v=vIH6Ow9ymKs

https://www.youtube.com/watch?v=vIH6Ow9ymKs



o SW#1 quality index describes velocity of the stress wave propagation along the fibres direction. High value of the velocity (or short time of flight) indicates material of high elasticity and therefore its high suitability for use in the timber structures.

• NIR#3 reaction wood, NIR#4 sapwood, NIR#5 for knot, NIR#7 for normal wood (measured in field, off-line on discs cut out from the processed logs, NIR spectra collected manually with an external sensor integrated with the SLOPE software system)

o NIR spectroscopy implemented within SLOPE pilot was used for detection of normal/sap wood presence/compositions as well as for detection of reaction wood and knots. Detection of decay, resin and bark was limited due to incompatibility of chemometric models not properly functioning in very low temperatures as noticed during the demonstration.

The following data were planned to be retrieved from wood discs of the eight fully processed logs transported and kept frozen at -20° C until measurement in the lab:

• HI#1 to HI#7 hyperspectral indices (planned to be collected by sensor scan bar in lab after the field demonstration based on the documented, collected and frozen wood discs)

o Implementation of the hyperspectral imaging technology within the SLOPE system is an extension of the NIR spectroscopy. The difference lies in the number of sensors utilized for the spectra acquisition and spectral range of interest. In the case of hyperspectral images 16 identical sensors are combined into an array and installed on the scanning bar. Hyperspectral imaging implemented within SLOPE project was planned to be used for detection of normal/sap wood presence/compositions as well as for detection of decay, reaction wood, knot, resin and bark. A 2D surface map of the quality can be scrutinized. Also other quality indexes (for example lignin content, calorific value, extractives presence) can be included to the SLOPE system, assuming that the reference samples containing given deficiency/property are available, and a developed chemometric model is positively validated. Due to the technical limitations (electrical noise associated with relatively long cables connecting custom hyperspectral camera with the SLOPE control system, the hyperspectral image acquisition and data analysis was performed off-line in the laboratory conditions.

Directly during the log processing in the forest, the quality of the logs was graded by non-certified experts in the field. This grading represented the basis to discuss



the similarities, differences, potential, advantages and shortcomings and of the SLOPE system with regard to the currently applied visual grading schemes.

• Expert judgement (by not certified experts) based on an existing grading rules for log/biomass (Austrian Practices for Timber Trading - Österreichische Holzhandelsusancen (ÖHU) based on the ÖNORM L 1021:2013 Measurement of round timber, the ÖNORM EN 1927-1:2008 Qualitative classification of softwood round timber ― Part 1: Spruces and firs and the FHP Guidelines for Industrial wood, described in more detail in deliverable D.4.1; (the used expert form and applied grading rules can be found in chapter 6)

The data collection process is highlighted by the scheme in Figure 2.

Figure 2: Scheme of the demo and evaluation of the quality control system

during the pilot demonstration in Annaberg, Austria.



3 Results of quality control

Due to the low number of processed logs (n=8) from 5 trees, the collected data from the automated system and non-certified experts are presented as individual case studies in a comparison table structured as shown below.

Demo of Quality Control Log information

Image of wood log Image of measured path with MicroNIR sensor

Tree species Site Date/Time Workers Sample Nr. Tree Nr.

Visual judgement log disc Image of wood disc Path for NIR measurements Diameter (cm)

Hyperspectral image: Light reflection map at

1000nm

NIR sap and reaction wood image:

Light green – sap wood Dark green – heart wood

Blue – reaction wood

Knots Eccentric pith Ring shakes Heart shakes Insect attack Stain Resin pockets Split

Visual judgement wood log HI derived image Image based on NIR data Length (cm)

map of knots position along the log shaft black line corresponds to the log contour (diameter)

Knots Sweep Split Spiral grain Taper Position and dimension of knots along the log shaft.

Visual Quality Sensor-based Quality Indices measured A CF1QI CF chain saw B CF2QI Delimbing C SW1QU Time of flight

Pulp & paper wood/good qual. NIR1QI Reaction wood Pulp & paper wood/bad qual. NIR2QI Sapwood

Verbal interpretation of results SLOPE Quality Index

Comments and comparison between both approaches

overall QI SLOPE global value construction wood Y – yes/suitable plywood N – not suitable fuel (value of QI) pulp quality class: A quality class: B quality class: C quality class: D




Tree species P. abies Site Annaberg, AT Date/Time 11.10.2016, 10:37 Workers Zitek/Kastner Sample Nr. 2917 Tree Nr. 10

Visual judgement log disc Image of wood disc Path for NIR measurements Diameter (cm) 19

NIR heart wood diameter:

22-3-3=16 cm

Knots No Eccentric pith Yes, < 10 % Ring shakes No Heart shakes No Insect attack No Stain little Resin pockets No Split No

Visual judgement wood log HI derived image Image based on NIR data Length (cm) 400

Knots No, < 1/rm Sweep No Split No Spiral grain No Taper < 1,5 cm/rm Position and dimension of knots along the log shaft.

Visual Quality Sensor-based Quality Indices measured A CF1QI CF chain saw 1.0 B CF2QI Delimbing 0.19 C SW1QU Time of flight 0.43

Pulp & paper wood/good qual. NIR1QI Reaction wood 1.0 Pulp & paper wood/bad qual. NIR2QI Sapwood 0.2


The quality of log# 2917 assessed by expert (B) and automatic

system (C) are comparable. The No. of knots might have been

underestimated by visual grading. The log can be used in demanding

downstream conversion, including construction sector or

plywood production

overall QI SLOPE 0.36 construction wood Y (0.39) plywood Y (0.38) fuel N (0.00) pulp Y (1.00) quality class: A N (0.37) quality class: B N (0.39) quality class: C Y (0.42) quality class: D Y (0.49)






NIR heart wood diameter: 22-2-4=16 cm

Knots Yes, < 5 cm, dead Eccentric pith Yes, < 10 % Ring shakes No Heart shakes No Insect attack No Stain No Resin pockets No Split No


Knots yes, > 1/rm Sweep No Split No Spiral grain No Taper < 1,5 cm/rm Position and dimension of knots along the log shaft.

Visual Quality Sensor-based Quality Indices measured A CF1QI CF chain saw 0,43 B CF2QI Delimbing 0,65 C SW1QU Time of flight 0,43

Pulp & paper wood/good qual. NIR1QI Reaction wood 0,20 Pulp & paper wood/bad qual. NIR2QI Sapwood 0,50


The quality of log# 2920 assessed by expert (C) and automatic

system (C) are comparable. The delimbing scheme only shows

very small knots. The log can be used in demanding downstream

conversion, including construction sector

overall QI SLOPE 0.34 construction wood Y (0.36) plywood N (0.27) fuel N (0.00) pulp Y (0.43) quality class: A N (0.37) quality class: B N (0.38) quality class: C Y (0.38) quality class: D Y (0.39)







28-4-0=24 cm

Knots Yes, > 5 cm, dead Eccentric pith No Ring shakes No Heart shakes No Insect attack No Stain No Resin pockets No Split No






The quality of log# 2929 assessed by expert (C) and automatic

system (C) are comparable. E.g. FC2QI, the delimbing index

indicates the increased number of knots. The log can be used in

demanding downstream conversion, including construction

sector or plywood production








31-2-3=26 cm

Knots No Eccentric pith Yes, > 10 % Ring shakes No Heart shakes No Insect attack No Stain No Resin pockets No Split No





Verbal interpretation of results SLOPE Quality Index The quality of log# 2954 assessed

by expert (C) and automatic system (C) are comparable. The

log can be still used as round wood log, including construction

sector or plywood production A knot was detected by MicroNIR, although not identified by visual observation. Excentric pith could be potentially detected by HSI. Both grading schemes detected the high number of knots along

the shaft.





Tree species P. abies Site Annaberg, AT Date/Time 11.10.2016, 16:40 Workers Zitek/Kastner Sample Nr. 2955

Tree Nr. 8 Visual judgement log disc Image of wood disc Path for NIR measurements

Diameter (cm) 25


26-3-2=21 cm

Knots No Eccentric pith No Ring shakes No Heart shakes No Insect attack No Stain No Resin pockets No Split Yes


Knots yes, > 1/rm Sweep No Split Yes Spiral grain No Taper < 1,5 cm/rm Position and dimension of knots along the log shaft.




by expert (C) and automatic system (<D) is very low. The log cannot be used in demanding

downstream conversion, including construction sector.

The split of the wood disc and of the wood log was hard to detect

by the visual NIR system. However, the stress wave values maybe gives an indication of the

impaired quality by the crack, both systems yielded a low quality, with the automated

system being more restrictive.

overall QI SLOPE 0.22 construction wood N (0.12) plywood Y (0.39) fuel N (0.00) pulp Y (0.00) quality class: A N (0.12) quality class: B N (0.09) quality class: C N (0.07) quality class: D N (0.03)






Diameter (cm) 21


21-0-5=16 cm

Knots Yes, int. grown 5-8 cm

Eccentric pith No Ring shakes No Heart shakes No Insect attack No Stain No Resin pockets No Split No


IMAGE MISSING Knots yes, > 1/rm Sweep No Split No Spiral grain No Taper < 1,5 cm/rm Position and dimension of knots along the log shaft.




by expert (C) and automatic system (D) are comparable

(SLOPE system more restrictive). Main factor for visual grading was

No. of knots per m. The log cannot be used in demanding


plywood production, when assessed with the SLOPE system. It is due to low values of CF1 and

CF2.

overall QI SLOPE 0.15 construction wood N (0.14) plywood N (0.08) fuel N (0.00) pulp Y (0.00) quality class: A N (0.15) quality class: B N (0.15) quality class: C N (0.17) quality class: D Y (0.14)

Demo of Quality Control



Log information



Diameter (cm) 23

)

NIR heart wood diameter: 24-2-4=18 cm

Knots No Eccentric pith Yes, > 10 % Ring shakes No Heart shakes No Insect attack No Stain Yes, little, react. w. Resin pockets No Split No






The quality of log# 2958 assessed by expert (C) and automatic system (C) are comparable. Although the No. of knots

prevents it to be a visual class B, the log can be used in demanding


plywood production


Demo of Quality Control



Log information



Hyperspectral image (not available)


18-4-4 = 10 cm

Knots No Eccentric pith Yes, > 10 % Ring shakes No Heart shakes No Insect attack No Stain Yes, react. w., sign. Resin pockets No Split No


Knots no, < 1/rm Sweep No Split No Spiral grain No Taper < 1,5 cm/rm Position and dimension of knots along the log shaft.




by expert (C) and automatic system (D) are comparable.

The low density of knots visually observed could clearly be also seen on the delimbing scheme. The significant reaction wood

impairment determined by visual assessment could also be seen by

the MicroNIR sensor. MicroNIR measurements additionally

detected a knot which was not identified by visual interpretation.

overall QI SLOPE 0.15 construction wood N (0.14) plywood N (0.12) fuel N (0.00) pulp Y (0.00) quality class: A N (0.15) quality class: B N (0.15) quality class: C N (0.14) quality class: D Y (0.12)



4 Interpretation of results

During the pilot demonstration in Annaberg, Austria, data from 8 logs belonging to 5 trees were collected and graded visually and by the automated grading system of SLOPE (Tab. 1). The following Quality Indexes were determined:

• CP#1 QI (chain saw): cutting force data • CF#2 QI (delimbing): debranching data • SW#1 QI (ToF): stress wave propagation velocity • NIR#3 reaction wood, NIR#4 sapwood, NIR#5 for knot, NIR#7 for normal

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2917 B (C)

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delimbing

C 0.36 1.00 0.19 0.43 0.20 1.00

2920 C No. of knots decisive C 0.34 0.43 0.65 0.43 0.20 0.50 2929 C No. of knots decisive C 0.48 0.24 0.41 0.77 1.00 0.00

2954 C Eccentric pith & No. of knots decisive C 0.48 0.27 0.23 0.90 1.00 0.50

2955 C No. of knots & split decisive D 0.22 0.00 0.00 0.11 1.00 1.00

2956 C No. of knots decisive D 0.15 0.00 0.00 0.54 0.20 0.50 2958 C No. of knots decisive C 0.47 0.44 0.40 0.90 0.60 1.00

2960 C Eccentric pith &

stain/reaction wood decisive by VG

D 0.15 0.00 0.34 0.21 0.20 0.50

Table 1: Comparison of grading results (visual grading by non-certified experts vs. SLOPE indicators from intelligent processor head); with comments on the

decisive criteria for the visual grading, the decisive criteria for the SLOPE grading are shown in bold, the lowest index values are shown in red.

The results show, that generally the results are comparable, with some cases, where the SLOPE sensor-based grading was more restrictive in classification. This is mainly the case for log 2955, 2956 and 2960 where the low values of the delimbing (CF#2 QI) and/or cutting force (CP#1 QI) caused a worse quality class. For the visual



grading in most cases the No. of knots per running meter was the main decisive criterion for the log quality, accompanied by eccentric pith or stain/reaction wood. The data of CP#2 QI provided very detailed information on the number and position of the knots along the log shaft, while the visual grading only performs a very generic judgement of the number of knots to determine the quality class.

The results also show, that the sensors are able to provide additional/complementary information on the properties of the wood log, that are not included in the visual quality grading (e.g. cutting force, stress wave propagation velocity, position and size of branches, sap-wood and heart-wood diameter) and have been not used for quality grading in the field so far. For example the SW#1 QI (ToF – time of flight) was indicative of the quality of the log especially in a situation where a split of the log and disc occurred, and hence could be interpreted as an indirect measure of this kind of impairment which is hard to detect with other based sensor based technologies. CP#1 QI (cutting force) completed the description of the mechanical properties of the timber assessed as the resistance of the material to the cross cutting by the chain saw. High value of cutting forces associated with elastic materials indicates superior quality.

Micro-NIR reconstructed images of the log based on NIR#4 sapwood and IR#7 for normal wood allowed for a detailed determination of the heart-wood diameter, a parameter very decisive of the quality of a log. Heart wood represents the more valuable proportion of the log and can be quantified by this approach. It has to be mentioned, that the temperature of the infrared detector under harsh field conditions might have an influence on the sensitivity of the sensor and hence the reliability of the data. Further sensor calibrations need to be carefully considered by including also an effect of the variation of the detector/sample temperature.



5 Conclusion

The SLOPE system and the visual system are generally in good agreement, but due to the low number of processed logs the statistical confidence of the results is rather limited. However, based on the individual cases, initial conclusions about the potential of the SLOPE automated grading system can be drawn and hypotheses for further research on the relation between the data provided by the different sensors and the mechanical material properties can be developed.

Compared to the visual grading system, which is based on the subjective grading of a human operator, the SLOPE system quality quantification aims at being well-defined, objective and repetitive trying to avoid subjective bias. However, still reliable correlations between the sensor data, mechanical wood properties and its potential uses need to be developed to reach this goal. Well calibrated sensors and models and the scientifically based definition of threshold values for the different parameters and their combinations are a pre-requisite for the SLOPE quality rating. Once the system is calibrated and the models established, the openness of the SLOPE grading system generally allows for the definition of further categories of resource uses improving further resource use along the production chain by improved description of material properties.

Summarizing, based on the existing experiences and the results from the pilot studies it can be concluded, that the SLOPE quality control system provides important complementary information enriching significantly the conventional visual grading approach. The automatic system is capable of improving the usage of the bio-resource along the production chain by the measurement of the described additional parameters. Initial hypotheses on the relation between the sensor data and the wood log quality as a basis for further scientific studies can be developed. If more logs will be processed in the future, and better correlations between the measured parameters and the log quality will be established, the accuracy and value of the novel system will be improved.



6 APPENDIX

6.1 Computation rules for quality indices

Table 2: Threshold values for the definition of the quality indices calculated based on different sensor data (in detail described in D.4.12); note that only those values are given, where data have been collected during the Annaberg

pilot study in Austria; all numbers are defined by experts, the QI threshold is a minimum value of QI defined by expert.

quality index

name value_min value_max QI treshold product #1: construction

wood

product #2: plywood

product #3: pulp

product #4: fuel quality class A quality class B quality class C quality class D

minimum score: 0,3 0,3 0,1 0,1 0,9 0,5 0,3 0,1CF1QI CF chain saw 1 10 0,3 1,0 0,5 0,8 0,7 0,6 0,5CF2QI delimbing 500 10000 0,3 1,0 0,2 1,0 0,8 0,5 0,3

SW1QU Time of flight 500 1500 0,5 1,0 0,0 1,0 0,8 0,7 0,4SW2QI Free vibrationsNIR1QI reaction wood 0,4 1,0 0,4 0,9 0,7 0,5 0,3NIR2QI sapwood 0,5 0,4 0,7 0,4 0,2 0,1NIR3QI barkNIR4QI rotNIR5QI knotNIR6QI resinNIR7QI normal woodHI1QI reaction woodHI2QI sapwoodHI3QI barkHI4QI rotHI5QI knotHI6QI resinHI7QI normal wood



6.2 Grading rules according to the Austrian Practices for

Timber Trading

Class A is used for all premium uses. It has to be healthy, with a straight shaft, without eccentric pith, spiral grain or knots. Only an absolute minimum of quality impairment is allowed.

Class B are all logs, that cannot be classified as A but that do not show bigger quality impairments than the following:

• Taper (Abholzigkeit): < 1.5 cm / rm • Knots (Astigkeit) on log shaft (judged also on log disc):

o < 29 mid diameter of logs: intergrown < 5 cm and/or dead < 3 cm intergrown 5-8 cm and/or dead 3-5 cm maximum number of knots is n= 1 / rm

o > 29 cm mid diameter of logs: intergrown < 5 cm and/or dead < 3 cm intergrown 5-8 cm and/or dead 3-5 cm maximum number of knots is n= 2 / rm

• Spiral grain (Drehwuchs) o < 29 mid diameter of logs:

a deviation of < 5 cm from log axis o > 29 cm mid diameter of logs:

a deviation of < 7 cm from log axis • Excentric pith (Buchs)

o < 10 % of total diameter of log disc • Ring shakes (Ringschäle)

o < 15 % of log disc diameter • Heart shakes

o Yes, if this is the only impairment • Insect attack

o No • Stain

o Only acceptable if the value of the wood is not impaired • Resin pockets

o Maximum 1 resin pocket < 5 cm on log disc • Sweep (Krümmung)

o one-sided < 15 of the mid-diameter, two-sided < 7 % of the mid-diameter

• Split (gespalten) o No split allowed



Class C are all logs, where the defined values of category B are exceeded, but the use as round wood log is still possible ( minimum diameter of 10 cm without bark applies, otherwise it becomes the class pulp & paper wood):

• Taper (Abholzigkeit): > 1.5 cm / rm • Knots (Astigkeit) on log shaft (judged also on log disc):

o Above values for class B • Spiral grain (Drehwuchs)

o Above values for class B • Eccentric pith (Buchs)

o < 50 % of total diameter of log disc • Ring shakes (Ringschäle)

o Above values for class B • Heart shakes

o Yes, if this is the only impairment • Insect attack

o Acceptable • Stain

o Only acceptable if the value of the wood is not impaired • Resin pockets

o More than one resin pocket < 5 cm on log disc • Sweep (Krümmung)

o one-sided 15-20 % of the mid-diameter, two-sided 7-10 % of the mid-diameter

• Split (gespalten) o If the split does not cover the whole diameter and the log can be

still used as round wood • Class Pulp & paper wood/good qual.

o All wood having impairments worse than allowed for category C, or have a smaller minimum diameter (minimum between 4-7 cm, depending on the company)

• Class Pulp & paper wood/bad qual. o All wood having impairments worse than allowed for category C,

or have a smaller minimum diameter (minimum between 4-7 cm, depending on the company)



6.3 Data collection form based on the grading rules according

to the Austrian Practices for Timber Trading

Tree species Workers

Site Time Date

Sample Nr. Storage time Pict. Nr.

Characteristics log disc (wood disc)

No/Yes

No/Yes

No/minor/existing

No/< 5 cm/> 5 cm

No/Yes

Wood log

No/Yes

No/Yes

Pulp & paper wood/good qual.possible: A B C Pulp & paper wood/bad qual.

Split

Spiral grain

Taper < 1,5 cm/rm / > 1,5 cm/rm

Quality:

Assessment form for the qualitative classification of softwood round timber based on Austrian Practices for Timber Trading

Split

Length

Knotsup to 29 cm mid-diameter 1/rm and from > 30 cm mid-diameter 2/rm

SweepNo/ one-sided 15-20 % of the mid-diameter, two-sided 7-10 % of the mid-diameter

Ring shakes No/Yes but max. < 15 % of the diameter/ > 15 % of diameter

Heart shakes

Insect attac

Stain

Resin pockets

Diameter

KnotsNo/intergrown < 5 cm - dead < 3 cm/intergrown 5-8 cm - dead 3-5 cm

Eccentric pith No/Yes but only max. 10% of the diameter/ > 10 % of the diameter

deliverable d.7.04 demo report for quality control · simple software tools capable of data...

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