Optimizing sawing of boards for furniture production using CT log scanning
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Abstract
The inherent variability of wood material together with sub-optimization in production processes means that a lot of potential value is lost. Computed tomography scanning together with simulation models of the production processes could remedy this, and ensure optimization of the entire production process. Therefore, the purpose of this study was to investigate if such methods can be used to optimize the sawing position of logs in a production process including further processing, in this case crosscutting to make a furniture product with strict quality requirements on dead knots. This was done on 47 Scots pine (Pinus sylvestris L.) logs. The results show a potential yield increase of more than 11 % points in the crosscutting operation and more than 4 % points when viewing the process as a whole, compared to sawing the logs horns down and centered.
Keywords
CT scanning Computer simulation Furniture Process control Scots pineReferences
- 1.Perstorper M, Pellicane PJ, Kliger IR, Johansson G (1995) Quality of timber products from Norway spruce. Wood Sci Technol 29(3):157–170CrossRefGoogle Scholar
- 2.Usenius A, Song T, Heikkilä A (2007) Optimization of activities throughout the wood supply chain. In: Blanchet P (ed) Proceedings of the International Scientific Conference on Hardwood Processing, Quebec City, Canada, 24–26 Sept 2007Google Scholar
- 3.Beenhakker HL (1964) Optimization versus suboptimization. Int J Prod Res 3(4):317–325CrossRefGoogle Scholar
- 4.Pulkki R (2001) Role of supply chain management in the wise use of wood resources. South Afr For J 191(1):89–95Google Scholar
- 5.Swedish Forest Industries Federation (2012) Skogsindustrin—en faktasamling. 2012 års branschstatistik (in Swedish). (English title: The forest industry-a collection of facts. Industry statistics of 2012)Google Scholar
- 6.Lindgren O (1991) Medical CAT-scanning: X-ray absorption coefficients, CT-numbers and their relation to wood density. Wood Sci Technol 25(5):341–349CrossRefGoogle Scholar
- 7.Zhu DP, Conners RW, Schmoldt DL, Araman PA (1996) A prototype vision system for analyzing CT imagery of hardwood logs. IEEE Trans Syst Man Cybern Part B Cybern 26(4):522–532CrossRefGoogle Scholar
- 8.Bhandarkar SM, Faust TD, Tang M (1999) Catalog: a system for detection and rendering of internal log defects using computer tomography. Mach Vision Appl 11(4):171–190CrossRefGoogle Scholar
- 9.Moberg L (2000) Models of internal knot diameter for Pinus sylvestris. Scand J Forest Res 15(2):177–187CrossRefGoogle Scholar
- 10.Alkan S, Zhang Y, Lam F (2007) Moisture distribution changes and wetwood behavior in subalpine fir wood during drying using high X-ray energy industrial CT scanner. Dry Technol 25(3):483–488CrossRefGoogle Scholar
- 11.Brüchert F, Baumgartner R, Sauter UH (2008) Ring width detection for industrial purposes-use of CT and discrete scanning technology on fresh roundwood. In: Gard WF, van de Kuilen JWG (eds) Proceedings of the COST E53 conference, Delft, Netherlands, 29–30 Oct 2008Google Scholar
- 12.Hou ZQ, Wei Q, Zhang SY (2009) Predicting density of green logs using the computed tomography technique. For Prod J 59(5):53–57Google Scholar
- 13.Longuetaud F, Mothe F, Kerautret B, Krähenbühl A, Hory L, Leban JM, Debled-Rennesson I (2012) Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples. Comput Electron Agric 85:77–89CrossRefGoogle Scholar
- 14.Wei Q, Leblon B, La Rocque A (2011) On the use of X-ray computed tomography for determining wood properties: a review. Can J Forest Res 41(11):2120–2140CrossRefGoogle Scholar
- 15.Giudiceandrea F, Ursella E, Vicario E (2011) A high speed CT scanner for the sawmill industry. In: Divos F (ed) Proceedings of the 17th International Non Destructive Testing and Evaluation of Wood Symposium, University of West Hungary, Sopron, Hungary, 14–16 Sept 2011Google Scholar
- 16.Magnusson Seger M, Danielsson PE (2003) Scanning of logs with linear cone-beam tomography. Comput Electron Agric 41(1):45–62CrossRefGoogle Scholar
- 17.An Y, Schajer G (2014) Feature-specific CT measurements for log scanning: theory and application. Exp Mech 54(5):753–762CrossRefGoogle Scholar
- 18.Rinnhofer A, Petutschnigg A, Andreu JP (2003) Internal log scanning for optimizing breakdown. Comput Electron Agric 41(1):7–21CrossRefGoogle Scholar
- 19.Lundahl CG, Grönlund A (2010) Increased yield in sawmills by applying alternate rotation and lateral positioning. For Prod J 60(4):331–338Google Scholar
- 20.Berglund A, Broman O, Grönlund A, Fredriksson M (2013) Improved log rotation using information from a computed tomography scanner. Comput Electron Agric 90:152–158CrossRefGoogle Scholar
- 21.Broman O, Fredriksson M (2015) Effect of raw material on yield in a furniture production process. In: Proceedings of the 22nd International Wood Machining Seminar, Quebec City, Canada, 14–17 June 2015Google Scholar
- 22.Fredriksson M, Berglund A, Broman O (2015) Validating a crosscutting simulation program based on computed tomography scanning of logs. Holz Roh Werkst 73(2):143–150CrossRefGoogle Scholar
- 23.Grönlund A, Björklund L, Grundberg S, Berggren G (1995) Manual för furustambank (in Swedish). (English title: manual for pine stem bank.) Technical Report 1995:19. Luleå University of Technology, Luleå, SwedenGoogle Scholar
- 24.Nordmark U (2005) Value recovery and production control in the forestry wood chain using simulation technique. Dissertation, Luleå University of Technology, SwedenGoogle Scholar
- 25.Chiorescu S, Grönlund A (1999) Validation of a CT-based simulator against a sawmill yield. For Prod J 50(6):69–76Google Scholar
- 26.Moberg L, Nordmark U (2006) Predicting lumber volume and grade recovery for Scots pine stems using tree models and sawmill conversion simulation. For Prod J 56(4):68–74Google Scholar
- 27.Fredriksson M (2014) Log sawing position optimization using computed tomography scanning. Wood Mater Sci Eng 9(2):110–119CrossRefGoogle Scholar
- 28.Mäkinen H (1999) Growth, suppression, death, and self-pruning of branches of Scots pine in southern and central Finland. Can J For Res 29(5):585–594CrossRefGoogle Scholar
- 29.Vestøl GI, Høibø OA (2000) Internal distribution of sound and dead knots in Picea abies (L.) Karst. Holz Roh Werkst 58(1–2):107–114Google Scholar
- 30.Moberg L (2006) Predicting knot properties of Picea abies and Pinus sylvestris from generic tree descriptors. Scand J For Res 21(S7):49–62CrossRefGoogle Scholar
- 31.Vuorilehto J, Tulokas T (2007) On log rotation precision. For Prod J 57(1/2):91–96Google Scholar
- 32.Johansson E, Johansson D, Skog J, Fredriksson M (2013) Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces. Comput Electron Agric 96:238–245CrossRefGoogle Scholar