WoodWisdom-Net Research Programme Final Report

Final Report 1(17)

New Forest Industry Production Systems

Based on High-speed CT Scanning (CT-Pro)

FINAL REPORT

Title of the research project New Forest Industry Production Systems Based on High-speed CT Scanning

Coordinator of the project Johan Skog

BASIC PROJECT DATA

Project period 01.10.2010 – 30.09.2013

Contact information of the coordinator SP Technical Research Institute of Sweden / SP Trä (institute/unit, address, telephone, fax, e-mail) Laboratorgränd 2 SE – 931 77 SKELLEFTEÅ SWEDEN Tel: +46-10-516 6247 E-mail: johan.skog@sp.se

URL of the project http:// http://www.sp.se/en/index/research/CT-Pro/

FUNDING

Total budget in EUR 1 671 689 EUR

Public funding from WoodWisdom-Net Research Total funding granted in EUR by source: Programme:

Germany Fachagentur Nachwachsende Rohstoffe (FNR) 301 342 EUR Sweden Swedish Governmental Agency for Innovation 457 600 EUR (4 500 000 SEK) Systems (VINNOVA)

Other public funding:

-

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Other funding:

MiCROTEC GmbH - srl, Italy 691 640 EUR

Träcentrum Norr (TCN), Sweden 158 931 EUR (1 414 485 SEK)

Dold Holzwerke GmbH, Germany 27 990 EUR

SCA Timber AB, Sweden 34 186 EUR (304 253 SEK)

PROJECT TEAM (main participants)

Name, degree, Sex Organization, For a visitor: Funder job title (M/F) graduate school organization & country of origin

Johan Skog, Ph D, M SP Vinnova / SP senior scientist, coordinator 2011-13

Johan Oja, Ph D, M SP Vinnova professor, coordinator 2010-11

Erik Johansson, M Sc, M SP / LTU Vinnova doctoral student

Erik Wernersson, Lic, M SP Vinnova senior scientist

Dennis Johansson, Ph D, M SP SP senior scientist

Anders Grönlund, Ph D, M LTU Vinnova / TCN professor

Magnus Fredriksson, Lic, M LTU Vinnova / TCN doctoral student

Anders Berglund, Lic, M LTU Vinnova / TCN doctoral student

Franka Brüchert, Ph D, F FVA FVA senior scientist

Norvin Laudon, M Sc, M FVA FVA senior scientist

Rafael Baumgartner, M FVA FNR M Sc, senior scientist

Udo H. Sauter, M FVA FVA Ph D, senior scientist

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Enrico Ursella, M Sc, M MiCROTEC MiCROTEC engineer

Enrico Vicario, M Sc, M MiCROTEC MiCROTEC engineer

DEGREES Degrees earned or to be earned within this project.

Year Degree Sex Name, year of birth and University Supervisor of thesis, (M/F) year of earning M.Sc., supervisor’s organization D.Sc., etc. Degree

2012 Lic.Eng. M Magnus Fredriksson, Luleå University Anders Grönlund, LTU b 1984, M.Sc. 2009, of Technology

2013 Lic.Eng. M Anders Berglund Luleå University Anders Grönlund, LTU b 1985, M.Sc.2011 of Technology

2013 Ph.D. M Johan Skog Luleå University Johan Oja, LTU 1980, M.Sc. 2004, of Technology Lic.Eng. 2009

2013 Lic.Eng. M Erik Johansson Luleå University Johan Oja, LTU 1986, M.Sc. 2010 of Technology

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ABSTRACT X-ray computed tomography (CT) is a well-known research tool in the field of wood science, but has always been too slow and not robust enough for use in an industrial environment. Within this project, a high-speed CT scanner has been developed; capable of reaching around 10 times higher speeds than any other industrial CT scanner. Image analysis algorithms have also been developed, capable of automatically extracting a large number of relevant parameters from the CT images of the logs. This is the first scanner of its kind, meaning that the project has opened up completely new possibilities of optimizing the log breakdown. Different production strategies have been evaluated, and it has been found that by optimizing the sawing position with respect to internal features of the log, there is a potential to increase the value of sawn goods by 10%–20%. With current positioning equipment, it is probably possible to utilize around half of this potential. This means that an important side-effect of the project is that manufacturers of sawmill equipment will be urged to produce machinery capable of positioning the logs much more accurately than today.

1.1 Introduction

1.1.1 Background CT scanning was introduced for medical applications in the 1970’s. Its value for non-destructive testing of wood was quickly recognized and for the last 30 years, CT scanning of logs has been a very important research application in the field of wood science and technology. By CT scanning, detailed information on the interior of the log can be obtained. Knowing the position of the most important log features has obvious benefits for production optimization. Already from the beginning, the vision has been to be able to CT scan logs at industrial speed. The technology has, however, always been too slow and not robust enough for use in an industrial environment. Recent scientific and technological breakthroughs, for example new algorithms for reconstruction of CT images, have however provided the technological prerequisites for the development of a new generation of faster CT scanners. This means that, at the beginning of this project, the vision of creating an industrial high-speed CT scanner was for the first time a realistic one. The advent of CT scanning in the sawmilling industry will also require new production strategies that take advantage of the detailed information provided by the scanners. 1.1.2 Objectives The objective of the CT-Pro project was to develop production strategies that focus on the whole process and, based on information from industrial high-speed CT scanning, increase the total value by more efficient raw material utilization and better customer adaptation. The project also includes the development of a high-speed CT scanner prototype, and algorithms specifically developed for analysis of data from high-speed CT scanning of logs.

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1.2 Results and discussion 1.2.1 High-speed CT scanner First, Microtec developed software capable of accurately simulating pictures of the quality that could be expected by a high-speed industrial CT scanner. This software includes the first industrial application ever of a Katsevich type reconstruction algorithm for wide cone beam tomography (Giudiceandrea et al. 2011). This tool has been very valuable for all other research tasks within the project as it allows the conversion of data previously recorded using medical or other high-resolution CT-scanners into high-speed CT scanner images. The realization of an industrial CT scanner at 120 m/min created a large number of problems to be solved. Some examples are the aerodynamics and centrifugal forces of a gantry rotating at 190 rpm, the data and power transmission to the rotating gantry and the computational power needed to get the results within a few seconds after the passage of each log. The first prototype was tested in the Microtec facilities and then installed at a sawmill for a slower application at 10 m/min (Figure 1). Then a second system was installed in a sawmill operating at 60 m/min and a third system is now working at a sawmill at full speed and with full resolution. In this third installation, logs and stems up to 25 m long are scanned at 120 m/min and the CT images are reconstructed about 1 meter after the corresponding cross-sections have completely passed the sensors. The main defects are automatically calculated and sent to a software that optimize the bucking process based on the information received.

Figure 1. The first installation of a high-speed CT scanner for logs.

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1.2.2 Image analysis software A survey was conducted among Swedish and German softwood sawmills, identifying the most important log features to detect using an industrial CT scanner. For Sweden, results were; knot positions, rot, knot types, knot size, bark thickness, heartwood content, splits and reaction wood. For Germany, results were; rot, foreign objects, knot sizes, splits, wood species identification, pruning (knot end), knot types and annual ring width. Within this project, software has been developed for automatically detecting all these features in high-speed CT images of logs (Figure 2). The ability to cut virtual boards has also been added to the software. These boards can then be graded and priced using existing Microtec software. This way, the value yield of a suggested cutting pattern can be calculated. Such calculations form the basis when optimizing the cutting of the log with respect to value and have been very important when evaluating various production strategies.

Figure 2. Left: Log with automatically detected features; sound knots (yellow), dead knots (red), splits (blue) and resin pockets (yellow). Right: Virtual break-down of log into boards. Heartwood shape, outer shape on bark and under bark Algorithms for heartwood shape and outer shape on bark were already available at project start. Because bark and dry sapwood are similar in density, the log shape under bark is somewhat difficult to determine. An algorithm considering both density and geometry has been developed. The validation of the automated bark detection show good agreement with reference measurements and the algorithm is capable of handling green logs as well as logs with minor and major drying. Knots Within the heartwood zone, knots are easily extracted. In sapwood, however, the density contrast to the surrounding wood is very low. Therefore, knots in sapwood are detected by tracing knots from the heartwood and carefully examining if they are still present at the expected position. The knot detection algorithm and observed measurement accuracies are presented by Johansson et al (2013). The algorithm exhibits high knot detection rates and root mean square errors of around 5 mm in knot diameter and 12 mm in dead knot border position. These results are good in comparison to previous studies on medical CT scanners. However, it is concluded that knot diameter errors are an important error source in the value optimization of the log. Therefore, future efforts should be spent on reducing the knot diameter error.

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Figure 3. Real board surface and corresponding CT image including automatically detected knots.

Figure 4: CT image of a Norway spruce log, showing the region of automatically detected rot (severe in yellow; discoloured in red). Rot Rot in an early state causes discoloration. In this stage of fungi attack, water is produced, causing an increase in density which can be observed in CT images of the log. If the decay develops further, the cell wall structure disintegrates and the wood loses its mechanical strength. In regions of severe rot, a lower density can be observed in the CT images. However, there are also intermediate stages of rot which do not show any density contrast to healthy wood, and therefore cannot be detected directly in the CT-images. Since all logs degraded by rot fungi will contain at least some early or late stages of decay, the algorithm is capable of detecting such logs, Figure 4. There is, however, some uncertainty regarding the extent of the decay/rot infected region. Resin pockets An algorithm capable of detecting resin pockets in heartwood has been developed, Figure 5. It is possible to find resin pocket-rich logs, making the algorithm suitable for log sorting purposes. However, because of a rather low detection rate and the inaccuracy of the saw-line positioning, it is not suitable to optimize sawing with respect to detected resin pockets.

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Figure 5: Real board surface and corresponding CT image including automatically detected resin pockets

Figure 6: Real board surface and corresponding CT image including automatically detected splits

Splits In CT images, open splits and checks appear darker than the surrounding wood as the occluded air causes a lower absorption of X-rays. In the detection algorithm developed in this project a double threshold is used. The lower threshold is used to detect the very low density areas in the CT-image. These areas were used as seeds for a region growing procedure into the areas derived by applying the second threshold, Figure 6. Annual rings Annual rings are often clearly visible within the heartwood. An algorithm has been developed that identifies regions where the rings are visible and calculates the local directions of the rings. This data can be used both for the detection of average annual ring width and the detection of irregular annual ring shapes.

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1.2.3 Production strategies Production strategies are made up of principle decisions regarding a large number of different parts of the production process, such as product mix, customer adaptation, size of mill, raw material mix, logistics et cetera. Consequently, the best strategy will differ from mill to mill. In the CT-Pro project we have focused our efforts on cases where there is a clear difference between a high-speed CT log scanner and the current generation of discrete X-ray log scanners. The biggest difference is that a CT scanner gives information about how different log features are located in the 3D-space, which makes CT scanning ideal for optimizing the log positioning. Sawing optimization Sawing simulations of about 600 Scots pine logs and 800 Norway spruce logs showed that there is a great potential value increase when rotating each log for greatest profit return, both when using visual appearance grading and visual strength grading. In all tested cases, the average value increased by 11-13% when using a rotation position that maximizes the value of each log instead of the conventional horns-down position (Figure 7). When optimizing also the parallel position and the skew of the sawing, the value increase potential was 21%. When introducing positioning errors, around half of the value potential was lost (Berglund et al. 2013). This will urge manufacturers of sawmill equipment to produce machinery capable of positioning the logs more accurately than today. It was also found that the correlation between the value optimized log rotation and the outer shape of the log is very weak. This means that the outer shape cannot be used as an indicator of how the log should be rotated for greatest profit return. Optimizing the log rotation based on CT scanning will mean a move away from the traditional horns-down position. However, choosing a different rotational position than horns down might be detrimental for the board shape after drying, especially for curved logs. Thus there was a need to investigate at what level of log curve it is possible to freely rotate the log without causing board warp. The results showed that for relatively straight logs, with a bow height of less than 15 mm, an unconventional rotational position did not cause excess spring in the dried boards (Figure 8). Bow and twist were not at all affected by the rotational position of the log. This means that roughly 75% of the Scandinavian logs can be rotated based on inner features without problems with excessive spring on the sawn boards.

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Figure 7: Value increase in percent (left) and change in volume yield in percentage points (right) for the value optimized log rotation of 800 spruce logs, compared to the horns down position.

Figure 8: Observed bow spring of the centre boards vs. automatically measured bow height. Plus symbols represent boards from logs sawn horns-down orientation and circular symbols represent boards from logs sawn with a rotation of 90° relative to the horns-down position. The quality limit represents the maximum allowed board spring for the highest board grade in the Nordic Timber Grading Rules.

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Log sorting The potential of using the CT scanner for log sorting has also been evaluated on spruce from south west of Germany. A test was conducted to evaluate the improvement in yield of visual board grades for multi-layer panel production by presorting the logs which will go into this production line, Figure 9. A total of 74 logs were CT scanned, sawn and split to lamellas. It was found that by presorting the logs and using only the better half of the logs, it was possible to decrease the amount of low grade (C) boards from 31% to 16%, Figure 10. This means that the same amount of A and B boards can be produced using 18% less input volume. By simulating an optimized sawing for the same set of logs, it was found that the yield of A boards could have been increased from 2% to 11% and the yield of C boards reduced from 31% to 21%, Figure 11.

Figure 9: The steps of the panel production trial: labelling of logs, CT scanning, sawing, kiln drying and splitting into thin boards

Figure 10: Pre-sorting: visual appearance class distribution for actually sawn boards and distribution of the boards sawn from the higher quality half

Figure 11: Optimised sawing by log rotation: visual appearance class distribution for actually sawn boards and distribution of the boards sawn with the optimal angle

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Positioning of the CT scanner The sawing optimization studies described above are based on the assumption that the CT scanner is used to control the positioning of the logs in the saw-line, suggesting that the saw-line would be a good position for the scanner. Studies performed by Microtec however shows that it might be possible to have the CT scanner located in the log yard for log sorting and trace the log identity and rotational position to the saw by using a 3D and an X-ray scanner in the saw-line. Positioning the CT scanner in the log sorting is advantageous as it also opens up for other applications, such as advanced log sorting or bucking operations.

1.3 Conclusions Industrial high-speed CT scanning is for the first time available to the wood processing industry. There is a value-increase potential of at least 10% by optimizing the sawing position. Logs with a bow height of less than 15 mm can be rotated freely without problems with spring on the sawn boards. The CT scanner can also be used for other applications, such as pre-sorting of logs for special purposes. In one trial, the same amount of high-grade boards could be produced using 18% less input volume, by sorting out the best half of the logs.

1.4a Capabilities generated by the project The project has generated the knowledge basis for three published licentiate theses and one doctoral thesis, as well as another three doctoral theses that will be complete within a year. Valuable research tools for simulating an industrial CT scanner and for feature extraction in CT images have been developed. The project has generated a ground-breaking new product, the high-speed CT scanner Microtec CT.Log. This product has the potential of revolutionizing the log breakdown. By knowing what you get before sawing, product qualities can be increased and the amount of discharged products is reduced. Apart from the sawing optimization covered by this project, the CT scanner has already found applications also for veneer production and bucking optimization.

1.4b Utilization of results A scanner with all the features required by the project is already in use at a sawmill. The automatic detection of the log defects is working and is continuously being improved. In this installation, the scanner is used only for the bucking optimization, while a big impact is expected in future applications where the internal information obtained by the scanner will be used also for the sawing optimization. This application will require some further development, though, especially the ability to trace the logs from the bucking station to the saw-line.

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A lot of potential customers have contacted Microtec and are very interested in installing CT scanners at their sawmills, but it is still difficult for the customers to understand the wide possibilities they will have to change their production strategies. The presentation at the Ligna fair 2013 was, however, a crucial and very effective moment for the sawmills to start understanding the benefits of and planning for future installations of CT scanners.

1.5 Publications and communication

a) Scientific publications

For publications indicate a complete literature reference with all authors and for articles a complete name. Indicate the current stage of the publishing process when mentioning texts accepted for publication or in print. Abstracts are not reported. Indicate the five most important publications with an asterisk.

1. Articles in international scientific journals with peer review *Berglund A, Broman O, Grönlund A, Fredriksson M (2013). Improved log rotation using information from a computed tomography scanner. Computers and Electronics in Agriculture 90, 152–158. *Breinig L, Berglund A, Grönlund A, Brüchert F, Sauter UH (2013). Effect of knot detection errors when using a computed tomography log scanner for sawing control. Forest Products Journal 63(7–8), 263–274. *Fredriksson M, Broman O, Persson F, Axelsson A, Ah Shenga P (2014). Rotational Position of Curved Saw Logs and Warp of the Sawn Timber. Wood Material Science and Engineering 9(1), 31–39. *Johansson E, Johansson D, Skog J, Fredriksson M (2013). Automated knot detection for high speed computer tomography in Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces. Computers and Electronics in Agriculture. 96, 238–245. Submitted: Berglund A, Johansson E, Skog J (2013). Value optimized log rotation for strength graded boards using computed tomography. Submitted to journal. Fredriksson M (2013). High Value Log Positioning using Computed Tomography Scanning. Submitted to journal. Fredriksson M, Johansson E, Berglund A (2013). Rotating Pinus sylvestris sawlogs by projecting knots from computed tomography images onto a plane. Submitted to journal.

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2. Articles in international scientific compilation works and international scientific conference proceedings with peer review Baumgartner R, Laudon N, Brüchert F, Sauter UH (2013). Virtual grading of boards using high-speed CT data compared to visual board grading. In: Proceedings of the 18th International Symposium on Nondestructive Testing and Evaluation of Wood, September 24-27, 2013, Madison, USA. FPL-GTR-226: 342-347 Fredriksson M, Berglund A, Johansson E (2013). Log Positioning by Aid of Computed Tomography Data. In: Proceedings of the 21st International Wood Machining Seminar (IWMS21), August 4-8, 2013 , Tsukuba , Japan. Giudiceandrea F, Ursella E, Vicario E (2011). A high speed CT scanner for the sawmill industry. In: Proceedings of the 17th International Nondestructive Testing and Evaluation of Wood Symposium, September 14-16, 2011, Sopron, Hungary. *Giudiceandrea F, Ursella E, Vicario E (2012). From research to market: a high speed CT scanner for the sawmill industry. XXXI Scuola Annuale di Bioingegneria, September 17-21, 2012, Bressanone, Italy. In: Bonfiglio A., Magenes G., Pietrabissa R., Gabriella M (eds.): Dalla ricerca al mercato trasformare il risultato della ricerca in un prodotto. Patron editore, Bologna: 159-169. Laudon N, Baumgartner R, Brüchert F, Sauter UH (2013). Automatic detection of pitch pockets in high speed industrial CT images of Norway spruce. In: Proceedings of the 18th International Symposium on Nondestructive Testing and Evaluation of Wood, September 24-27, 2013, Madison, USA. FPL-GTR-226: 15-21. 3. Articles in national scientific journals with peer review - 4. Articles in national scientific compilation works and national scientific conference proceedings with peer review - 5. Scientific monographs Berglund A (2013). Process control and production strategies in the sawmill industry, Licentiate thesis. Luleå Tekniska Universitet, Skellefteå, Sweden. Fredriksson M (2012). Computer simulation in the forestry-wood chain, Licentiate thesis. Luleå Tekniska Universitet, Skellefteå, Sweden. Johansson E (2013). Computed Tomography of Sawlogs – Knot Detection and Sawing Optimization. Licentiate thesis, Luleå Tekniska Universitet, Skellefteå, Sweden.

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Skog J (2013). Characterization of sawlogs using industrial X-ray and 3D scanning. Doctoral thesis, Luleå Tekniska Universitet, Skellefteå, Sweden. 6. Other scientific publications, such as articles in scientific non-refereed journals and publications in university and institute series Sauter UH, Breinig L, Brüchert F (2010). Towards a quality optimised timber production: measurement of knots in roundwood prior to sawing using CT. International Forestry Review, Vol. 12 (5): 297. Brüchert F, Sauter UH (2011). Was können die diskrete Röntgentechnologie und die Computer-tomographie zur Optimierung der Forst-Holz-Kette beitragen? AFZ-Der Wald Nr. 11: 24-26. Grönlund A, Fredriksson M (2011). Fingerjointing Simulation: First Step to Complete Integration. FDM Asia, October 2011, pp. 34-37. ISSN: 0218-7663. b) Other dissemination

Oct. 2010: Press release about the the start of CT-Pro in NTT såg & trä, trade magazine Oct. 2010: Press release about the the start of CT-Pro in AFZ, trade magazine (Makkonen K.) Oct. 28th 2010: Presentation of CT-Pro at FP Innovations, Vancouver, Canada. (Oja J) Nov. 23rd 2010: Presentation of CT-Pro at Moelven technology meeting, Karlstad, Sweden. (Oja J) Feb. 1-2nd 2011: Presentation: 3rd WoodWisdom-Net Research Seminar 2011, Paris. (Oja J) Feb. 2-3rd 2011: Poster presentation at ITF Automationsdagar, Stockholm. (Johansson E) March 11th 2011: Project website (http://www.sp.se/en/index/research/CT-Pro) May 5th 2011: Presentation of CT-Pro at FVA Kuratoriumssitzung (biannual meeting of FVA’s external scientific board). (Brüchert F) June 17th 2011: Presentation of CT-Pro at Forst-BW Tübingen, Germany. (Brüchert F) Sept. 2011: WoodWisdom-Net Newsletter September 2011, p. 10. (Skog J) Nov. 29th 2011: Presentation of CT-Pro at Annual meeting of the NFZ Wood quality laboratories, Nancy, France. (Brüchert F)

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Jan. 2012: Annual report 2011 of SP Technical Research Institute of Sweden: Ny röntgenteknik kan ge miljardförbättring inom träindustrin (New X-ray technology may increase yield in wood industry by billions of Swedish crowns). (Skog J) Jan. 20th 2012: Computer-Tomographie (computer tomography), 7. Int. Kongress Säge- und Holzindustrie, Würzburg, 20. Jan. 2012. (Giudiceandrea F) Feb. 7-8th 2012: Project presentation at 4th WoodWisdom-Net Seminar, Helsinki, Finland. (Johansson D, Skog J) May 2012: Project presentation, Sawmill days, Värö, Sweden. (Johansson D). July 8-12th, 2012: Use of X-ray for Detection of Internal Log Features, IUFRO All. Div. 5 – Lisbon, Portugal. (Grönlund A, Skog J) July 8-12th, 2012: Automatic Detection of Fungal Wood Decay in high-speed Computed Tomography Images, IUFRO All. Div. 5 – Lisbon, Portugal. (Laudon N, Baumgartner R, Brüchert F, Sauter UH) Aug. 2012: Project presentation “Medical X-ray technology of the future is being used in sawmills”. Trä och teknik, Gothenburg, Sweden. (Johansson D) Sep. 27-29th, 2012: Launch of Industrial CT.Log, Innovation Festival Bozen-Bolzano, Italy. (Giudiceandrea F) Oct. 18-19th 2012: Scanning for Real-Virtual-Reality in wood products engineering - From Industrial CT log-scanning in full speed to 3D measurements of a timber bridge movements. Optik & Fotonikdagarna 2012, Hudiksvall, Sweden. (O Hagman). Nov. 2012: New CT scanner can reshape wood industry. SP Trä Newsletter Nov 2011. (Skog J) Nov. 2012: Snart kan skiktröntgen av stockar vara verklighet (Soon computer tomography of logs is reality) Nordisk träteknik, NTT 2012:23 Jan. 2013: CT-Pro. In: Branschforskningsprogrammet för skogs- och träindustrin – Projektkatalog 2013. Vinnova Information VI 2013:01, pp. 140-141. (Skog J) Jan. 2013: EARTO (European Association of Research & Technology Organizations, magazine “Impact delivered” 2012: p. 8. Feb. 20-21st, 2013: 5th WoodWisdom-Net Research Programme Seminar, Munich, Germany. (Skog J) May 6-10th, 2013: LIGNA 2013 – Stall hall 27, E 21. May 2013 onwards: Online information of project on LIGNA 2013 web service.

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May 2013: Holz-Zentralblatt – Sonderheft Ausstellerverzeichnis LIGNA 2013, p. 224. May 21st, 2013: What’s under the surface? – Principles and technology behind X-ray based imaging systems. Seminar at Luleå Tekniska Universitet, Skellefteå, Sweden. (Skog J) June 3rd 2013, July 2nd 2013: Forschung für die Praxis: Computertomografie in Waldwachs-tumskunde und Waldnutzung. Seminar at ForstBW, Freiburg, Germany. (Brüchert F, Sauter UH) July 1-5th 2013: Wide cone beam tomography for industrial application, oral presentation at Tomography of Materials and Structures, Ghent, Belgium. (Giudiceandrea F, Ursella E, Vicario E) July 1-5th 2013: Sub-mm width measurement of cracks in industrial computer tomographic scans of softwood tree discs, poster presentation at Tomography of Materials and Structures, Ghent, Belgium. (Laudon N, Brüchert F, Wehrhausen M, Sauter UH)

1.6 National and international cooperation The project consortium consisted of partners from Sweden, Germany and Italy, meaning that both national and international cooperation has been integral parts of the project. A total of seven physical project meetings have been held, three in Sweden, two in Germany and two in Italy. These project meetings have included project discussions, sawmill excursions and team-building activities and have been crucial for both the progress of the project and for the development of close relationships between the project partners. The project has also included collaboration via staff exchange. Throughout the whole project, one researcher has been working part time at FVA (Germany) and part time at Microtec (Italy) and one doctoral student has been working part time at SP (Sweden) and part time at LTU (Sweden). Two doctoral students from LTU have spent time at FVA and one doctoral student from FVA has spent time at LTU. The transnational collaboration has been important for the project, making sure that the special requirements of both the northern European and central European forest industries have been considered within the project. By meeting with research and industry partners from three different countries, we have also learnt a great deal from one another and built valuable connections for future research projects. There has been a close collaboration between research and industry, especially in work packages 4–6 where the research partners SP and FVA have been working together with industrial partner Microtec on a common software for analysis of CT images. Also the work on the production strategies (WP 2, 8, 9 and 10) has been conducted in close collaboration between industry, who have defined the requirements on the technology, and the research partners LTU and FVA who have conducted most of the research.