Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length
This study explores the predictive modeling of surface roughness in the hard turning of AISI 4340 steel using machine learning techniques, specifically Random Forest (RF) and Gaussian Process Regression (GPR). The uncoated carbide was used and the experimental design considered varying cutting speed...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tamkang University Press
2025-06-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202601-29-01-0003 |
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