Tool Condition Monitoring in machining for the workpiece surface quality evaluation
Paper i proceeding, 2024

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.

Tool Condition Monitoring

Surface Quality



Antonio Del Prete

Universita del Salento

Lars Nyborg

Chalmers, Industri- och materialvetenskap, Material och tillverkning

Rodolfo Franchi

Universita del Salento

Teresa Primo

Universita del Salento

Materials Research Proceedings

24743941 (ISSN) 2474395X (eISSN)

Vol. 41 2011-2020
9781644903131 (ISBN)

27th International ESAFORM Conference on Material Forming, ESAFORM 2024
Toulouse, France,


Produktionsteknik, arbetsvetenskap och ergonomi


Bearbetnings-, yt- och fogningsteknik



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