ABSTRACT

Computed tomography (CT) scanning of logs makes appearance-grading virtual sawn timber possible before the log is sawn. A CT-scanner can measure the knot structure inside a scanned log, inferring how to saw the log. The knot structure of virtual sawn timber was graded as being suitable or not for a specific product by the existing rule-based approach and used to create a set of descriptive statistical variables used by two machine learning models. The PLS models were trained on two quality references; the quality grade of the finished product or the image-grade based on images of the sawn timber, extracted from the dry-sorting station's automatic grading system and graded by two experienced researchers. The results show that the two PLS models perform equally well when sorting sawn timber to the customer, indicating that the quality references are equally useful for training a PLS model. The PLS models both delivered 93% of the dried sawn timber to the customer, leaving very little sawn timber with customer-specific properties at the sawmill, of which 89% and 90% of the delivered sawn timber passed the intended product's quality demands. The rule-based approach delivered 85% dried sawn timber with a 73% pass rate.

摘要

原木的计算机断层扫描 (CT) 扫描使得在锯切原木之前对虚拟锯材进行外观分级成为可能。CT 扫描仪可以测量扫描原木内部的结结构，推断如何看到原木。通过现有的基于规则的方法，虚拟锯材的节结构被分级为适合或不适合特定产品，并用于创建一组由两个机器学习模型使用的描述性统计变量。PLS 模型接受了两个质量参考的训练；成品的质量等级或基于锯材图像的图像等级，从干选站的自动分级系统中提取，并由两名经验丰富的研究人员进行分级。结果表明，两种 PLS 模型在将锯材分拣给客户时表现同样出色，表明质量参考对于训练 PLS 模型同样有用。PLS 模型都向客户交付了 93% 的干燥锯材，在锯木厂留下的具有客户特定特性的锯材很少，其中 89% 和 90% 的交付锯材通过了预期产品的质量要求。基于规则的方法交付了 85% 的干锯材，合格率为 73%。