Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Applied Sciences

AbdullahM. Almeshal¹  MahdiS. Alajmi² 

[1] Department of Electronic Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait;Department of Manufacturing Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait;

关键词: artificial intelligence;    machine learning;    cutting forces;    Gaussian process regression;   

DOI  :  10.3390/app11094055

来源: DOAJ

【 摘 要 】

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R² = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

【 授权许可】

Unknown