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Abstract. Achieving high surface quality is crucial in manufacturing, impacting product functionality and appearance. Poor quality can lead to defects, friction, and safety risks. Cutting tools endure harsh conditions and wear over time, affecting surface quality and increasing costs. Monitoring tool condition is vital for efficiency, reducing cycle times and downtime. Industries like aerospace and automotive require tight quality control for meeting standards. Historically, manual inspections and scheduled changes were used, but advanced technology now allows more efficient tool condition monitoring. The paper outlines a tool condition monitoring approach using sensors and machine learning to predict and classify tool conditions and workpiece surface quality. It integrates acoustic emission, accelerometer, and thermal infrared camera sensors into a lathe machine. Various machine learning algorithms are trained and validated to accurately predict tool and surface conditions. The most effective model is identified and presented.
Antonio Del Prete (Fri,) studied this question.