A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays crucial role manufacturing components. addition to monitoring, prediction assists systems making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model predict turning wear. Cutting forces, roughness specimen, flank insert are measured for corresponding spindle speed, feed rate, depth cut. Those test datasets applied predictions. The was conducted using SNMG TiN Coated Silicon Carbide EN8 steel specimen. dataset is extracted from 200 tests with varied Random forest regression, Support vector K Nearest Neighbour regression algorithms used R squared, technique shows random predicts 91.82% accuracy validated experimental trials. results exhibit mainly influenced by rate followed speed lower can be achieved finish workpiece. proposed may helpful decisions, which leads achieving machined Moreover, adaptable cloud environments intelligent systems.