Influence of Support Vector Regression (SVR) on Cryogenic Face Milling
The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9984369 |
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| author | Rao M. C. Karthik Rashmi L. Malghan Fuat Kara Arunkumar Shettigar Shrikantha S. Rao Mervin A. Herbert |
| author_facet | Rao M. C. Karthik Rashmi L. Malghan Fuat Kara Arunkumar Shettigar Shrikantha S. Rao Mervin A. Herbert |
| author_sort | Rao M. C. Karthik |
| collection | DOAJ |
| description | The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error. |
| format | Article |
| id | doaj-art-4b333871b26f4243a21df3de6ff45928 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-4b333871b26f4243a21df3de6ff459282025-02-03T07:23:58ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/99843699984369Influence of Support Vector Regression (SVR) on Cryogenic Face MillingRao M. C. Karthik0Rashmi L. Malghan1Fuat Kara2Arunkumar Shettigar3Shrikantha S. Rao4Mervin A. Herbert5Department of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, IndiaDepartment of Artificial Intelligence and Data Science, Angadi Institute of Technology and Management, Savagaon, Karnataka, IndiaDepartment of Mechanical Engineering, Duzce University, Duzce, TurkeyDepartment of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, IndiaDepartment of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, IndiaDepartment of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, IndiaThe paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.http://dx.doi.org/10.1155/2021/9984369 |
| spellingShingle | Rao M. C. Karthik Rashmi L. Malghan Fuat Kara Arunkumar Shettigar Shrikantha S. Rao Mervin A. Herbert Influence of Support Vector Regression (SVR) on Cryogenic Face Milling Advances in Materials Science and Engineering |
| title | Influence of Support Vector Regression (SVR) on Cryogenic Face Milling |
| title_full | Influence of Support Vector Regression (SVR) on Cryogenic Face Milling |
| title_fullStr | Influence of Support Vector Regression (SVR) on Cryogenic Face Milling |
| title_full_unstemmed | Influence of Support Vector Regression (SVR) on Cryogenic Face Milling |
| title_short | Influence of Support Vector Regression (SVR) on Cryogenic Face Milling |
| title_sort | influence of support vector regression svr on cryogenic face milling |
| url | http://dx.doi.org/10.1155/2021/9984369 |
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