Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Advanced machining processes and optimization · Advanced Sensor Technologies Research · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Manufacturing Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait
Department of Electronic Engineering Technology, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait
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