Prediction of surface roughness using a novel approach
Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining paramet...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Iran University of Science & Technology
2021-09-01
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| Series: | International Journal of Industrial Engineering and Production Research |
| Subjects: | |
| Online Access: | http://ijiepr.iust.ac.ir/article-1-1192-en.html |
| _version_ | 1818723440176136192 |
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| author | M Kaladhar VSS Sameer Chakravarthy PSR Chowdary |
| author_facet | M Kaladhar VSS Sameer Chakravarthy PSR Chowdary |
| author_sort | M Kaladhar |
| collection | DOAJ |
| description | Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining parameters. Consequently, enumerating the good relation between surface roughness (Ra) and machining parameters is a highly focused task. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness in hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). When compared with RSM models, the proposed FPA based model showed the least percentage of mean absolute error. The model obtained the strongest correlation coefficient value of 99.75% among the other models values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is a better choice over the RSM based regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models. |
| first_indexed | 2024-12-17T21:10:33Z |
| format | Article |
| id | doaj.art-23add7092399493192a231e3108a1afd |
| institution | Directory Open Access Journal |
| issn | 2008-4889 2345-363X |
| language | English |
| last_indexed | 2024-12-17T21:10:33Z |
| publishDate | 2021-09-01 |
| publisher | Iran University of Science & Technology |
| record_format | Article |
| series | International Journal of Industrial Engineering and Production Research |
| spelling | doaj.art-23add7092399493192a231e3108a1afd2022-12-21T21:32:28ZengIran University of Science & TechnologyInternational Journal of Industrial Engineering and Production Research2008-48892345-363X2021-09-01323113Prediction of surface roughness using a novel approachM Kaladhar0VSS Sameer Chakravarthy1PSR Chowdary2 Department of Mechanical Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India Department of Electronic and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India Department of Electronic and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining parameters. Consequently, enumerating the good relation between surface roughness (Ra) and machining parameters is a highly focused task. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness in hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). When compared with RSM models, the proposed FPA based model showed the least percentage of mean absolute error. The model obtained the strongest correlation coefficient value of 99.75% among the other models values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is a better choice over the RSM based regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.http://ijiepr.iust.ac.ir/article-1-1192-en.htmlhard turningsurface roughnessregressionflower pollination algorithm |
| spellingShingle | M Kaladhar VSS Sameer Chakravarthy PSR Chowdary Prediction of surface roughness using a novel approach International Journal of Industrial Engineering and Production Research hard turning surface roughness regression flower pollination algorithm |
| title | Prediction of surface roughness using a novel approach |
| title_full | Prediction of surface roughness using a novel approach |
| title_fullStr | Prediction of surface roughness using a novel approach |
| title_full_unstemmed | Prediction of surface roughness using a novel approach |
| title_short | Prediction of surface roughness using a novel approach |
| title_sort | prediction of surface roughness using a novel approach |
| topic | hard turning surface roughness regression flower pollination algorithm |
| url | http://ijiepr.iust.ac.ir/article-1-1192-en.html |
| work_keys_str_mv | AT mkaladhar predictionofsurfaceroughnessusinganovelapproach AT vsssameerchakravarthy predictionofsurfaceroughnessusinganovelapproach AT psrchowdary predictionofsurfaceroughnessusinganovelapproach |