Mechanical Quantities Prediction of Metal Cutting by Machine Learning and Simulation Data

Abstract

Metal cutting is an important process in industrial manufacturing. Using the mechanical quantities of metal cutting to optimize process design is helpful to improve productivity. However, it is expensive to obtain these quantities due to the complexity of the cutting process, including material nonlinearity, geometric nonlinearity, state nonlinearity and their interactions. In this paper, a prediction model is constructed by combining machine learning (ML) and simulation data to quickly acquire multi-difficult-to-obtain metal cutting mechanical quantities to solve this problem. First, Adaptive Smoothed Particle Hydrodynamics (ASPH) is used to generate a simulation dataset of 2000 metal cutting cases. Based on the simulation data, six machine learning (ML) methods are employed to establish two prediction models, single-task learning and multi-task learning, to predict the mechanical quantities of metal cutting. The experimental results demonstrate that the ML method can predict abundant reference data efficiently after understanding the relationship between simulation parameters and mechanical quantities from simulation data, which is expected to replace some similar and repetitive simulation work. The Multilayer Perceptron (MLP) model under the multi-task setting provides the best prediction performance, fastest prediction time efficiency, and stable model behavior. Additionally, input erasure experiments reveal that the prediction of maximum equivalent plastic strain is significantly affected by particle spacing, and cutting speed plays a vital role in predicting maximum velocity. This work highlights the promotion of the data-driven ML method in quickly obtaining abundant reference data for the metal cutting process, and provides an auxiliary means for process optimization.