Multivariate product adapted grading of Scots pine sawn timber for an industrial customer, part 1: Method development

Abstract

Rule-based automatic grading (RBAG) of sawn timber is a common type of sorting system used in sawmills, which is intricate to customise for specific customers. This study further develops an automatic grading method to grade sawn timber according to a customer's resulting product quality. A sawmill's automatic sorting system used cameras to scan the 308 planks included in the study. Each plank was split at a planing mill into three boards, each planed, milled, and manually graded as desirable or not. The plank grade was correlated by multivariate partial least squares regression to aggregated variables, created from the sorting system's measurements at the sawmill. Grading models were trained and tested independently using 5-fold cross-validation to evaluate the grading accuracy of the holistic-subjective automatic grading (HSAG), and compared with a re-substitution test. Results showed that using the HSAG method at the sawmill graded on average 74% of planks correctly, while 83% of desirable planks were correctly identified. Results implied that a sawmill sorting station could grade planks according to a customer's product quality grade with similar accuracy to HSAG conforming with manual grading of standardised sorting classes, even when the customer is processing the planks further.