Multivariate product adapted grading of Scots Pine sawn timber for an industrial customer,part 2: Robustness to disturbances

ABSTRACT

Holistic-subjective automatic grading (HSAG) of sawn timber by an industrial customer's product outcome is possible through the use of multivariate partial least squares discriminant analysis (PLS-DA), shown by part one of this two-part study. This second part of the study aimed at testing the robustness to disturbances of such an HSAG system when grading Scots Pine sawn timber partially covered in dust. The set of 308 clean planks from part one of this study, and a set of 310 dusty planks, that by being stored inside a sawmill accumulated a layer of dust, were used. Cameras scanned each plank in a sawmill's automatic sorting system that detected selected feature variables. The planks were then split and processed at a planing mill, and the product grade was correlated to the measured feature variables by partial least squares regression. Prediction models were tested using 5-fold cross-validation in four tests and compared to the reference result of part one of this study. The tests showed that the product adapted HSAG could grade dusty planks with similar or lower grading accuracy compared to grading clean planks. In tests grading dusty planks, the disturbing effect of the dust was difficult to capture through training.     

Customer adapted grading of Scots pine sawn timber using a multivariate method

To define new grading rules, or to customize the ones in use in a rule-based automatic grading (RBAG) system of boards, is a time-consuming job for a sawmill engineer. This has the effect that changes are rarely made. The objective of this study was to continue the development of a method that replaces the calibration of grading rule settings by a holistic-subjective automatic grading, using multivariate models. The objective was also to investigate if this approach can improve sawmill profitability and at the same time have a satisfied customer. For the study, 323 Scots pine (Pinus sylvestris L.) boards were manually graded according to the preferences of an important customer. That is, a customer that regularly purchases significant volumes of sawn timber. This manual grading was seen as reference grading in this work. The same boards were also scanned and graded by a RBAG system, calibrated for the same customer. Multivariate models for prediction of board grade based on aggregated knot variables, obtained from the scanning, were calibrated using partial least squares regression. The results show that prediction of board grades by the multivariate models were more correct, with respect to the manual grading, than the grading by the RBAG system. The prediction of board grades based on multivariate models resulted in 76–87% of the boards graded correctly, according to the manual grading, while the corresponding number was 63% for the RBAG system.