Development of ML Model for Tool Life Prediction

This project aims to study the tool life of cutting tools in CNC machines using machine learning techniques. By analysing parameters like cutting speed, feed rate, depth of cut, and material properties, the model predicts tool life based on experimental data. This results in increased productivity, reduced downtime, and enhanced machining efficiency. It also explores predictive maintenance and quality control in industrial areas. Machine learning algorithms, such as regression and classification models, are evaluated for predicting tool life. The study’s findings demonstrate how machine learning may be used to accurately predict the life of CNC tools, giving manufacturers the ability to plan preventative maintenance procedures. Machining operations can be managed to reduce production disruptions and tool replacement costs by anticipating wear and breakdown of the tools.