A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining

Abstract

In machining, specific cutting forces and temperature fields are of primary interest. These quantities depend on many machining parameters, such as the cutting speed, rake angle, tool-tip radius, and uncut chip thickness. The finite element method (FEM) is commonly used to study the effect of these parameters on the forces and temperatures. However, the simulations are computationally intensive and thus, it is impractical to conduct a simulation-based parametric study for a wide range of parameters. The purpose of this work is to present, as a proof-of-concept, a hybrid methodology that combines the finite element method (FE method) and machine learning (ML) to predict specific cutting forces and maximum tool temperatures for a given set of machining conditions. The finite element method was used to generate the training and test data consisting of machining parameter values and the corresponding specific cutting forces and maximum tool temperatures. The data was then used to build a predictive model based on artificial neural networks. The FE models consist of an orthogonal plane-strain machining model with the workpiece being made of the Aluminum alloy Al 2024-T351. The finite element package Abaqus/Explicit was used for the simulations. Specific cutting forces and maximum tool temperatures were calculated for several different combinations of uncut chip thickness, cutting speed and the rake angle. For the machine learning-based predictive models, artificial neural networks were selected. The neural network modeling was performed using Python with Adam as the training algorithm. Both shallow neural networks (SNN) and deep neural networks (DNN) were built and tested with various activation functions (ReLU, ELU, tanh, sigmoid, linear) to predict specific cutting forces and maximum tool temperatures. The optimal neural network architecture along with the activation function that produced the least error in prediction was identified. By comparing the neural network predictions with the experimental data available in the literature, the neural network model is shown to be capable of accurately predicting specific cutting forces and temperatures.

Finite Element Modeling and Experimental Validation of AA 5052-H34 Machining: A Comprehensive Study on Chip Morphology and Temperature Analysis

“…Modeling and simulation techniques are universal methods for understanding and optimizing machining processes. Several analytical and numerical models have been performed using FEM [10,11]. The J-C constitutive equation offers an insightful depiction of the behavior of metallic materials subjected to extensive strains, high strain rates, and temperature-dependent visco-plasticity [12].…”

“…The J-C constitutive equation offers an insightful depiction of the behavior of metallic materials subjected to extensive strains, high strain rates, and temperature-dependent visco-plasticity [12]. According to the literature, the J-C constitutive model has been widely and commonly used as a material model in the simulation of the machining processes [7,[10][11][12][13][14][15][16]. For instance, Mabrouki et al [12] conducted numerical and experimental studies on the dry cutting of the AA 2024-T351.…”