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

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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.