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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Tamkang University Press
2025-06-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202601-29-01-0003 |
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| author | A. Ginting M.S. Kasim B.T. Hang Tuah Baharudin |
| author_facet | A. Ginting M.S. Kasim B.T. Hang Tuah Baharudin |
| author_sort | A. Ginting |
| collection | DOAJ |
| description | 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 speeds ( 70, 90, 110 m/min ), feeds ( 0.14, 0.16, 0.22 mm/rev ), depths of cut ( 0.25, 0.5 mm ), and cutting length ( 80, 160, 240 mm ) to account for tool wear as an uncontrollable factor. The RF model achieved an RMSE of 0.1627µ m and an R^2 value of
0.9718 , while the GPR model had an RMSE of 0.1676µ m and an R^2 value of 0.8333 . The novelty of this study lies in considering the influence of tool wear via cutting length, significantly impacting the RMSE of the GPR model. Using K-fold cross-validation (K=7) on a 50% training dataset resulted in the lowest RMSE values for both models. Despite the GPR model’s slightly lower performance, it demonstrated robustness and consistency across different cross-validation splits and random states, making it a reliable option for predicting surface roughness. This research provides insights into the application of machine learning for process optimization in hard turning operations, highlighting the importance of tool wear and training dataset size. Future work could extend these findings to other machining processes and material types to validate the models’ robustness and generalizability. |
| format | Article |
| id | doaj-art-346890c34a6b4b95b51d5748393a619f |
| institution | Matheson Library |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tamkang University Press |
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| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-346890c34a6b4b95b51d5748393a619f2025-06-25T11:08:47ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-06-01291233110.6180/jase.202601_29(1).0003Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting LengthA. Ginting0M.S. Kasim1B.T. Hang Tuah Baharudin2Laboratory of Machining Processes, Department of Mechanical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Jalan Almamater, J17.01.01, Kampus USU, 20155, Medan, IndonesiaCADCAM Research Group, Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Kampus Gong Badak, 23100, Kuala Nerus, Terengganu, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, UPM Selangor, Selangor Darul Ehsan, MalaysiaThis 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 speeds ( 70, 90, 110 m/min ), feeds ( 0.14, 0.16, 0.22 mm/rev ), depths of cut ( 0.25, 0.5 mm ), and cutting length ( 80, 160, 240 mm ) to account for tool wear as an uncontrollable factor. The RF model achieved an RMSE of 0.1627µ m and an R^2 value of 0.9718 , while the GPR model had an RMSE of 0.1676µ m and an R^2 value of 0.8333 . The novelty of this study lies in considering the influence of tool wear via cutting length, significantly impacting the RMSE of the GPR model. Using K-fold cross-validation (K=7) on a 50% training dataset resulted in the lowest RMSE values for both models. Despite the GPR model’s slightly lower performance, it demonstrated robustness and consistency across different cross-validation splits and random states, making it a reliable option for predicting surface roughness. This research provides insights into the application of machine learning for process optimization in hard turning operations, highlighting the importance of tool wear and training dataset size. Future work could extend these findings to other machining processes and material types to validate the models’ robustness and generalizability.http://jase.tku.edu.tw/articles/jase-202601-29-01-0003cutting lengthgaussian process regressionhard turningmodel performancerandom forest |
| spellingShingle | A. Ginting M.S. Kasim B.T. Hang Tuah Baharudin Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length Journal of Applied Science and Engineering cutting length gaussian process regression hard turning model performance random forest |
| title | Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length |
| title_full | Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length |
| title_fullStr | Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length |
| title_full_unstemmed | Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length |
| title_short | Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length |
| title_sort | machine learning based surface roughness prediction in turning of hardened aisi 4340 steels incorporating tool wear via cutting length |
| topic | cutting length gaussian process regression hard turning model performance random forest |
| url | http://jase.tku.edu.tw/articles/jase-202601-29-01-0003 |
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