Neural Network Classifiers to Grade Parts Based on Surface Defects with Spatial Dependencies

Schmoldt, Daniel L.

doi:10.1007/978-1-4615-1987-4_99

Cited by 3 publications

(3 citation statements)

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“…In earlier approaches to lumber grading software, the upper grades FAS, F1F, and Selects were combined into a single grade (Boden et al, 2005), or F1F and Selects were not considered (Schmoldt, 1995). An F1F board must meet the minimum size requirements for a FAS board: 6-in.…”

Section: Methodsmentioning

confidence: 99%

“…http://dx.doi.org/10.1016/j.compag.2016.11.018 0168-1699/Published by Elsevier B.V. Schmoldt (1995) proposed an artificial neural network (ANN) classifier approach to grading parts and lumber that would be suitable for real-time processing operations. The best performing neural network configuration achieved an accuracy of 61.5%.…”

Section: Introductionmentioning

confidence: 99%

“…This network used standard back-propagation learning and consisted of 3 layers and 19 input nodes, 15 hidden layer nodes, and 5 output nodes. Both Schmoldt's (1995) and Boden et al's (2005) approaches grouped the upper grades: FAS, F1F, and Selects together and treated the common grades separately. Schmoldt's ANN approach also classified boards as below grade if they did not meet 3A Common specifications.…”

Section: Introductionmentioning

confidence: 99%

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An artificial neural network for real-time hardwood lumber grading

Thomas

2017

Computers and Electronics in Agriculture

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a b s t r a c tComputerized grading of hardwood lumber according to NHLA rules would permit fast assessment of sawn lumber and the evaluation of potential edging and trimming operations to improve lumber value. More importantly, to enable optimization of the hardwood lumber sawing process, a fast means of evaluating the potential value of boards before they are sawn is necessary. As log and lumber scanning systems become prevalent and common, these needs become more pressing. From an automation perspective, the NHLA lumber grades are difficult to implement efficiently in a computer program. Exhaustive approaches that examine every potential cutting size and combination to determine the grade give accurate grading solutions, at the cost of computation time. Other approaches have examined heuristic methods that implement key parts of the grading rules, or used artificial neural network methods, both with the loss of accuracy. Here, a different approach to computerized grading is examined that takes a hybridized approach using projected yield from cut-up simulation and neural network methods. This new hybrid approach has the advantage of both accuracy and high-processing speed. Such an approach lends itself to log sawing optimization with respect to NHLA grades and market values when internal log defect information is known.Published by Elsevier B.V.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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