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.     

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.     

A multivariate approach to automatic grading of Pinus sylvestris sawn timber

Abstract

The objective of this study was to create an easier way to handle the often complicated and intricate situations with which the operator of an automatic grading system is faced each time a change to the grading rules is proposed. The scope of the study was the possibility of a holistic method of automatic appearance grading of sawn wood similar to manual grading and based on multivariate statistics. The study was based on 90 Scots pine (Pinus sylvestris L.) sawlogs. The logs were sawn and the boards were scanned and manually graded. The result of the manual grading was defined as the true grade. Models for prediction of board grade based on aggregated defect variables were calibrated using partial least squares regression. The classification based on the multivariate models resulted in 80–85% of the boards being correctly graded according to the manual grading. In conclusion, this paper shows that a multivariate statistical approach for grading timber is a possible way to simplify the process of grading and to customize the grading rules when using an automatic grading system.