Handling positioning errors when optimizing sawing of Scots pine and Norway spruce logs using CT scanning

Published Date

First online: 07 June 2016

Title 

Handling positioning errors when optimizing sawing of Scots pine and Norway spruce logs using CT scanning

• Author 

• Magnus Fredriksson

Abstract

Since computed tomography (CT) X-ray scanning is becoming a reality in sawmills, different studies have been made to establish how well the sawing position of a log can be optimized using CT data. It is also known that positioning errors have an adverse effect on optimization, since the optimization methods used are rather sensitive to positioning errors. To mitigate the effect of positioning errors, a method is proposed in this article that filters results produced by sawing simulation, using a Gaussian filter of a size according to the positioning error. Using these filtered values for optimization, it is possible to retain two percent extra value of the sawn timber, when rotation and offset errors are present, compared to a regular optimization method. A method more robust to positioning errors is more useful in practice, since positioning errors of various magnitudes are always present in sawmills. The main contribution of this paper is, therefore, an optimization method that reduces the effect of positioning errors.

Keywords

CT scanning Sawmilling Optimization Positioning

References

1. 1.
   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 2011
2. 2.
   Magnusson Seger M, Danielsson PE (2003) Scanning of logs with linear cone-beam tomography. Comput Electron Agric 41:45–62CrossRef
3. 3.
   An Y, Schajer G (2014) Feature-specific CT measurements for log scanning: theory and application. Exp Mech 54:753–762CrossRef
4. 4.
   Rinnhofer A, Petutschnigg A, Andreu JP (2003) Internal log scanning for optimizing breakdown. Comput Electron Agric 41:7–21CrossRef
5. 5.
   Lundahl CG, Grönlund A (2010) Increased yield in sawmills by applying alternate rotation and lateral positioning. For Prod J 60:331–338
6. 6.
   Berglund A, Broman O, Grönlund A, Fredriksson M (2013) Improved log rotation using information from a computed tomography scanner. Comput Electron Agric 90:152–158CrossRef
7. 7.
   Fredriksson M (2014) Log sawing position optimization using computed tomography scanning. Wood Mater Sci Eng 9:110–119CrossRef
8. 8.
   Fredriksson M (2015) Optimizing sawing of boards for furniture production using CT scanning technique. J Wood Sci 61:474–480CrossRef
9. 9.
   Todoroki C (2003) Accuracy considerations when optimally sawing pruned logs: internal defects and sawing precision. Nondestruct Test Eval 19:29–41CrossRef
10. 10.
    Wessels CB (2009) Cant sawing log positioning optimization: a simulation study. For Prod J 59:17–22
11. 11.
    Tulokas T, Tannous J (2010) Research method and improvement of log rotation in sawmills. Silva Fennica 44:141–154CrossRef
12. 12.
    Vuorilehto J, Tulokas T (2007) On log rotation precision. For Prod J 57:91–96
13. 13.
    Yerbury MD, Cooper RJ (2010) Curve sawing spruce sawlogs containing sweep can reduce drying distortion when compared with conventional sawing. Forestry 83:443–450CrossRef
14. 14.
    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, Sweden
15. 15.
    Berggren G, Grundberg S, Grönlund A, Oja J (2000) Improved spruce timber utilization (STUD). European shared cost research project within FAIR (DGXII/E2), contract no. FAIR-CT96-1915. Final report sub-task A 1.2, database and non-destructive “glass-log” measurements. Technical report, AB Trätek and Luleå University of Technology
16. 16.
    Nordmark U (2005) Value recovery and production control in the forestry wood chain using simulation technique. Dissertation, Luleå University of Technology, Sweden
17. 17.
    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 96:238–245CrossRef
18. 18.
    Anonymous (1997) Nordic timber: grading rules for pine (Pinus sylvestris) and spruce (Picea abies) sawn timber: commercial grading based on evaluation of the four sides of sawn timber. Föreningen svenska sågverksmän (FSS), Sweden
19. 19.
    Chiorescu S, Grönlund A (2000) Validation of a CT-based simulator against a sawmill yield. For Prod J 50:69–76
20. 20.
    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 56:68–74
21. 21.
    Sederholm J (1980) Stockvis mätning och beräkning av sågutbytet (in Swedish with English summary). (English title: Measurement and calculation of the sawing yield log by log.) Technical report: STFI-Meddelande, Svenska Träforskningsinstitutet no. 602
22. 22.
    Sederholm J (1984) Optimerat sågutbyte förutsätter noggrann mätning och många kontroller (in Swedish with English summary). (English title: An optimal saw yield requires accurate measuring and control.) Dissertation, KTH Royal Institute of Technology, Sweden
23. 23.
    Øvrum A (2001) Innleggingsnøyaktighet i sagmaskiner (in Norwegian). (English title: Feeding accuracy into sawing machines.) Technical report 48. NTI Norwegian Institute of Wood Technology
24. 24.
    Law AM (2007) Simulation modeling and analysis, 4th edn. McGrawHill, New York

For further details log on website :
http://link.springer.com/article/10.1007/s10086-016-1566-3