A Tool Condition Monitoring Study to Support Circular Economy

CNC (Computer Numerical Control) machines are vital for precision and efficiency in manufacturing but are prone to tool wear, causing disruptions and sustainability challenges. This study introduces a project aimed at sustainable CNC tool management within the circular economy framework, focusing on extending tool lifespan through predictive analytics. Real-time monitoring predicts optimal tool replacement times, promoting reuse, repair, and recycling. The methodology includes data collection, preprocessing, anomaly detection, real-time analysis, and machine learning model selection, with the Random Forest model proving most effective. Unique contributions include the integration of advanced sensor data with AI-driven predictive maintenance, and the application of circular economy principles to CNC tool management. The results highlight significant accuracy in tool condition categorization, contributing to waste reduction and sustainable practices in manufacturing.