Cutting force estimation in turning of AISI 1117 free-cutting steel using machine learning algorithms

Abstract

Machine learning is widely used in several scientific domains for data prediction. Predicting cutting forces and temperature distribution in the domain of machining, a subdivision of manufacturing techniques, is crucial for enhancing production procedures. Studies in this topic frequently employ experimental methods and the finite element method, a numerical computation technique. Estimation algorithms can be employed to aid experimental and numerical computation procedures due to their lengthy cost and duration. This study analysed several machine learning algorithms and determined that the Cubic Support Vector Machine and Gaussian Process Regression (GPR) methods yielded the most comparable results.

Keywords

• Cubic Support Vector Machine
• Machine Learning
• Gaussian Process Regression

References

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