Metadata about: Abstract In this paper, machine learning (ML) models, namely Support Vector Regression (SVR) and Gaussian Process Regression (GPR), are presented for the prediction of specific cutting forces and maximum tool temperatures during orthogonal machining processes. The training/test data for building the ML models is generated from finite element (FE) simulations. The simulations are performed using the commercial FE package Abaqus/Explicit and validated using experimental results. The FE generated data consists of cutting speed, uncut chip thickness, and rake angle as the input parameters. The response variables are the cutting force and maximum tool temperature. The data is split into training and test sets using 80-20 split. The optimal SVR and GPR models are selected using grid search on the training data. The predictions on the test data sets show that both the models perform well with high accuracy in predicting cutting force and maximum tool temperature. Between the two models, the mean square errors (MSE) for SVR are less than those for GPR.

About: Abstract In this paper, machine learning (ML) models, namely Support Vector Regression (SVR) and Gaussian Process Regression (GPR), are presented for the prediction of specific cutting forces and maximum tool temperatures during orthogonal machining processes. The training/test data for building the ML models is generated from finite element (FE) simulations. The simulations are performed using the commercial FE package Abaqus/Explicit and validated using experimental results. The FE generated data consists of cutting speed, uncut chip thickness, and rake angle as the input parameters. The response variables are the cutting force and maximum tool temperature. The data is split into training and test sets using 80-20 split. The optimal SVR and GPR models are selected using grid search on the training data. The predictions on the test data sets show that both the models perform well with high accuracy in predicting cutting force and maximum tool temperature. Between the two models, the mean square errors (MSE) for SVR are less than those for GPR.

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