Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel
In this study, experimental investigations and simulations were conducted to examine the influence of machining parameters during the turning process of C45 medium carbon steel, with the simultaneous application of the Johnson-Cook plasticity model and Johnson-Cook damage model. The validity of the...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-02-01
|
| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251318170 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832086539903959040 |
|---|---|
| author | Tien-Thinh Le Hang Thi Pham Hiep Khac Doan Panagiotis G. Asteris |
| author_facet | Tien-Thinh Le Hang Thi Pham Hiep Khac Doan Panagiotis G. Asteris |
| author_sort | Tien-Thinh Le |
| collection | DOAJ |
| description | In this study, experimental investigations and simulations were conducted to examine the influence of machining parameters during the turning process of C45 medium carbon steel, with the simultaneous application of the Johnson-Cook plasticity model and Johnson-Cook damage model. The validity of the computational results was confirmed by comparing them with experimental findings on chip morphology and temperature rise during cutting. The obtained results indicate that cutting depth significantly affects chip morphology during the turning process, while cutting speed has a minimal impact on the chip length at a cutting depth of 1 mm. Additionally, the temperature rise during cutting is primarily concentrated around the cutting tool nose, rather than near the chip or machined surface. Among the investigated cutting speeds, 0.46 m/s is identified as the most suitable for turning C45 steel at a cutting depth of 1 mm. Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose. |
| format | Article |
| id | doaj-art-cf69bcd29a3749abaec48abbe7679b29 |
| institution | Kabale University |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-cf69bcd29a3749abaec48abbe7679b292025-02-06T13:03:40ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-02-011710.1177/16878132251318170Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steelTien-Thinh Le0Hang Thi Pham1Hiep Khac Doan2Panagiotis G. Asteris3Phenikaa Research and Technology Institute, A&A Green Phoenix Group JSC, Trung Hoa, Cau Giay, Hanoi, VietnamFaculty of Mechanical Engineering, Thuyloi University, Dong Da, Hanoi, VietnamFaculty of Mechanical Engineering, Thuyloi University, Dong Da, Hanoi, VietnamComputational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, Athens, GreeceIn this study, experimental investigations and simulations were conducted to examine the influence of machining parameters during the turning process of C45 medium carbon steel, with the simultaneous application of the Johnson-Cook plasticity model and Johnson-Cook damage model. The validity of the computational results was confirmed by comparing them with experimental findings on chip morphology and temperature rise during cutting. The obtained results indicate that cutting depth significantly affects chip morphology during the turning process, while cutting speed has a minimal impact on the chip length at a cutting depth of 1 mm. Additionally, the temperature rise during cutting is primarily concentrated around the cutting tool nose, rather than near the chip or machined surface. Among the investigated cutting speeds, 0.46 m/s is identified as the most suitable for turning C45 steel at a cutting depth of 1 mm. Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose.https://doi.org/10.1177/16878132251318170 |
| spellingShingle | Tien-Thinh Le Hang Thi Pham Hiep Khac Doan Panagiotis G. Asteris Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel Advances in Mechanical Engineering |
| title | Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel |
| title_full | Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel |
| title_fullStr | Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel |
| title_full_unstemmed | Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel |
| title_short | Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel |
| title_sort | experimental and computational investigation of the effect of machining parameters on the turning process of c45 steel |
| url | https://doi.org/10.1177/16878132251318170 |
| work_keys_str_mv | AT tienthinhle experimentalandcomputationalinvestigationoftheeffectofmachiningparametersontheturningprocessofc45steel AT hangthipham experimentalandcomputationalinvestigationoftheeffectofmachiningparametersontheturningprocessofc45steel AT hiepkhacdoan experimentalandcomputationalinvestigationoftheeffectofmachiningparametersontheturningprocessofc45steel AT panagiotisgasteris experimentalandcomputationalinvestigationoftheeffectofmachiningparametersontheturningprocessofc45steel |