A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear

Abstract

The cutting process is a complex nonlinear system. Predicting such a system with conventional regression models is inefficient. In this paper, a hybrid approach using deep neural networks (DNN) is proposed to predict the specific cutting forces. With the aim of obtaining the hybrid training data, orthogonal cutting tests and 2D FEM chip formation simulations have been performed under diverse cutting parameters, tool geometries and tool wear conditions. Predictive models using a DNN and a conventional linear regression method were established. In comparison with the conventional linear regression method, the hybrid model using the machining learning is more accurate.