Predicting Cutting Forces in Aluminum Using Polynomial Classifiers

Abstract

Abstract Due to increased calls for environmentally benign machining processes, there has been focus and interest in making processes more lean and agile to enhance efficiency, reduce emissions and increase profitability. One approach to achieving lean machining is to develop a virtual simulation environment that enables fast and reasonably accurate predictions of various machining scenarios. Polynomial Classifiers (PCs) are employed to develop a smart data base that can provide fast prediction of cutting forces resulting from various combinations of cutting parameters. With time, the force model can expand to include different materials, tools, fixtures and machines and would be consulted prior to starting any job. In this work, first, second and third order classifiers are used to predict the cutting coefficients that can be used to determine the cutting forces. Predictions obtained using PCs are compared to experimental results and are shown to be in good agreement.