An integrated 3D log processing optimization system for hardwood sawmills in central Appalachia, USA

Lin, Wenshu; Wang, Jingxin

doi:10.1016/j.compag.2011.12.014

Cited by 15 publications

(31 citation statements)

References 11 publications

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“…To get such a control value, other studies perform mill cuts (e.g. Thawornwong et al 2003;Chang and Gazo 2009;Lin and Wang 2012 ). One can argue, however, that a decision made by one sawyer in one selected mill is not necessarily representative for the average yield in an average sawmill.…”

Section: Discussionmentioning

confidence: 99%

Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection

Stängle

Brüchert

Heikkilä

et al. 2014

Annals of Forest Science

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& Context One of the most important wood defects affecting the value yield from European beech (Fagus sylvatica [L.]) logs is knots that are visible on the sawn wood surface. The non-invasive technology of X-ray computed tomography (CT) can be used for the assessment of log internal features, especially the geometry and position of knots before primary breakdown to support the decision of value-optimised log rotation in sawmills. & Aims The objective of this study was to test whether valueoptimised log rotation can be performed successfully by using the CT-derived knowledge of internal knottiness for the hardwood species beech. & Methods Size parameters of 670 knots were measured and their position was marked in CT images from 33 logs. The 3Dreconstructed logs were virtually sawn in 12 different rotational angles using the software InnoSIM. This allowed visual grading of the simulated sawn wood and the calculation of product volume and value. & Results The results show that if optimal rotation was applied to each single log, both total volume as well as total product value yield could be improved by up to 24 % compared with the average yield of all simulated rotational angles. & Conclusion In this small-scale study, it is demonstrated that CT technology could be used to support the decision about optimal rotational angle of beech logs to maximise volume and value yield.

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Section: Discussionmentioning

confidence: 99%

Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection

Stängle

Brüchert

Heikkilä

et al. 2014

Annals of Forest Science

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“…Regardless of the simplicity of the equipment used in Mozambique, to process the tropical hardwood species, the processing of the hardwood is in general difficult because of the large diameters and considerable irregularities in the logs. Many studies to increase the volume yield of hardwood have been conducted (Richards et al 1979;Meimban et al 1992;Lin and Wang 2012;Schajer 2016). There have also been extensive studies regarding the effects of log positioning, mainly for the softwood species from the "boreal region."…”

Section: A) B)mentioning

confidence: 99%

The Effect of Log Position Accuracy on the Volume Yield in Sawmilling of Tropical Hardwood

Shenga¹,

Bomark²,

Broman³

et al. 2016

BioResources

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This study investigated the effect of the positioning of the log before sawing on the volume yield of sawn timber from tropical hardwood species. Three positioning parameters were studied, the offset, skew, and rotation, combined with two sawing patterns of cant-sawing and through-andthrough sawing. A database consisting of two tropical hardwood species with very different outer shapes, jambirre (Millettia stuhllmannii Taub.) and umbila (Pterocarpus angolensis DC.), was used to simulate the sawing process. The result of the simulation revealed that, according to the combined effect of offset, skew, and rotation positioning, the positioning of the log before sawing is extremely important to achieve a high volume yield of sawn timber. The positioning parameter that has the highest effect on the volume yield is the rotation, and the variation in the volume yield associated with a deviation in the positioning can reduce the volume yield of sawn timber by between 7.7% and 12.5%.

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“…This question is especially concerned by the general trend towards digitization for forest wood-chain traceability, supply chain optimization, and transformation (Pickens et al, 1997;Lin and Wang, 2012;Gardiner and Moore, 2014;Müller et al, 2019). After the overall shape characterization defining material yield, and wood quality information coming from the cross-sectional ends in the case of roundwood, wood quality is mainly assessed from singularities of the bark surface.…”

Section: Introductionmentioning

confidence: 99%

A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

Nguyen

Constant

Kerautret

et al. 2020

Computers and Electronics in Agriculture

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Three-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These singularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epicormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86.

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An integrated 3D log processing optimization system for hardwood sawmills in central Appalachia, USA

Cited by 15 publications

References 11 publications

Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection

Potentially increased sawmill yield from hardwoods using X-ray computed tomography for knot detection

The Effect of Log Position Accuracy on the Volume Yield in Sawmilling of Tropical Hardwood

A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

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