Developing a continuous quality control method for the sawmilling process using sensory features and advanced data mining approaches

锯木厂不断调整以提高其回收率和生产率。然而，大多数对锯木厂中使用的周边切割工艺进行的研究（即，削片机、跑步机和锯）的试验已经在实验室环境中进行。虽然这种类型的研究对于更好地了解工具-木材相互作用中涉及的切削动力学至关重要，但将结果应用或外推到苛刻的锯木条件下可能具有挑战性。此外，它是很难设计最佳的切割系统的基础上，现有的文献，因为发表的研究结果往往是矛盾的。因此，研究计划的长期目标是开发周边切割模型（即，锯切），以超越现有的主要集中在正交切割的技术。本研究计划有五个具体目标：1）量化各种切削参数对不同响应变量的影响; 2）将不同传感器的感觉特征与自变量和因变量相关联; 3）在实验室规模上开发新的数据挖掘和人工智能（AI）方法来描述和预测锯切过程; 4）在工业背景下验证所开发的方法; 5）使用工业大数据集构建多目标模型。拟议方法包括两个阶段。第1阶段将在实验室环境中进行，其中变量得到精确控制，而第2阶段将在工业环境中进行。该项目将包括九个不同的步骤。第1-5步将是第1阶段的一部分，旨在评估切削参数对响应变量的影响，并探索新的数据挖掘和人工智能方法。第6-9步是第2阶段的一部分，重点是了解实验室和工业环境之间的根本差异，以及它如何影响切削动力学，并验证第1阶段开发的模型。根据结果，可以编程新的模型。从该项目中获得的知识将涉及切削动力学以及新的数据挖掘和人工智能方法在木材加工领域的应用。实验室和工业环境之间的相互关系也将得到更好的理解，以便促进技术进步和研究结果的实施。预期结果的相关性涉及流程优化和产品价值最大化，从而鼓励将结果转化为实践。同时，开发的模型将有利于木材行业向工业4.0过渡，从而提高质量控制和监测计划的有效性。 研究结果将在与研究领域相关的同行评审期刊上传播，并在会议上发表。因此，来自木材加工社区的同事将能够建立在这些发现的基础上，进一步推进我们对周边切削中工具木材动力学的理解。

Sawmills are constantly adapting to improve their recovery and productivity. However, most of the research done on peripheral cutting processes used in sawmills (i.e., chippers, canters and saws) has been conducted in a laboratory setting. While this type of research is essential to better understand the cutting dynamics involved in the tool-wood interaction, results can be challenging to apply or extrapolate to the harsh sawmilling conditions. Moreover, it is difficult to design optimal cutting systems based on the available literature since the published findings are often contradictory. The long-term objective of the research program is therefore to develop models for peripheral cutting (i.e., sawing) to move beyond the existing ones that mainly focus on orthogonal cutting. Five specific objectives are related to the research program: 1) quantify the impact of various cutting parameters on different response variables; 2) correlate sensory features of different sensors with the independent and dependent variables; 3) develop novel data mining and artificial intelligence (AI) approaches at the laboratory scale to describe and predict the sawing process; 4) validate the developed methods in an industrial context; 5) build a multi-objective model with an industrial large dataset. The proposed methodology is comprised of two phases. Phase 1 will be conducted in a laboratory environment, where variables are precisely controlled, while Phase 2 will be conducted in an industrial setting. The project will consist of nine distinct steps. Steps 1-5 will be part of Phase 1, aiming to evaluate the impact of cutting parameters on response variables and exploring novel data mining and AI approaches. Steps 6-9 are part of Phase 2, focusing on understanding the fundamental differences between laboratory and industry settings and how it influences the cutting dynamics, and validating the models developed in Phase 1. Depending on the results, new models may be programmed. The knowledge gained from the project will relate to the cutting dynamics as well as the application of novel data mining and AI approaches in the field of wood machining. The interrelation between laboratory and industrial environments will also be better understood so the implementation of technological advancements and findings can be facilitated. The relevance of the expected findings relates to process optimization and product value maximization, thus encouraging translation of findings into practice. In parallel, the developed models will be beneficial to the wood industry in transitioning towards Industry 4.0, thus improving the efficacy of quality control and monitoring programs. Results will be disseminated in peer-reviewed journals relevant to the field of research, and presented in conferences. Consequently, colleagues from the wood machining community will be able to build on these findings, further advancing our understanding of the tool-wood dynamics in peripheral cutting.