Machine Learning-Driven Force Analysis for Tool Wear Prediction Systems

Abstract

A system designed to forecast tool wear by utilizing a force sensor to monitor the wear of the tool’s flank and applying a Convolutional Neural Network (CNN) for forecasting purposes. The methodology is demonstrated through experiments in milling, utilizing dry machining with a ball endmill on a stainless-steel component. The flank wear of the tool is directly assessed using a digital microscope throughout the operation. The forecasts produced by the system’s machine learning model are based on a database filled with data from past experiments. Future versions of the system will be enhanced with an adaptive control (AC) system, which will continuously interact with the machine learning model to fine-tune feed rate and spindle speed, thereby optimizing flank wear management and extending tool lifespan. The adjustments by the AC system will be guided by the forecasts from the machine learning model and the force sensor readings, which can detect alterations in cutting forces indicative of tool flank wear. The primary objective of this study is to demonstrate the machine learning model, especially through CNNs, capability in predicting tool wear with an anticipated accuracy of 90%. Further experiments will be conducted to validate these results and to expand the measurement scope to enhance the system’s accuracy.

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