Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material
CP-Ti G2 has become the preferred biocompatible material for various devices mainly used in orthopedic and dental implants and it is also used in aviation and aircraft. While CP-Ti G2 deals with good ductility, higher stiffness, and fatigue resistance. The novelty of present research work was attent...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-09-01
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| Series: | Journal of the Mechanical Behavior of Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jmbm-2021-0005 |
| _version_ | 1818744660992983040 |
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| author | Kumar Anish Sharma Renu Gupta Arun Kumar |
| author_facet | Kumar Anish Sharma Renu Gupta Arun Kumar |
| author_sort | Kumar Anish |
| collection | DOAJ |
| description | CP-Ti G2 has become the preferred biocompatible material for various devices mainly used in orthopedic and dental implants and it is also used in aviation and aircraft. While CP-Ti G2 deals with good ductility, higher stiffness, and fatigue resistance. The novelty of present research work was attentive to the effect of WEDM factors on MRR. After machining, surface topography was examined through SEM. MRR was modeled through ANOVA to analyze the adequacy. It was observed that POT, POFT, PC, and SGV most significant factors. The WEDM factors have also been significantly deteriorating the morphology of machined samples in the form of craters, debris, and micro cracks. A multi-objective optimization ‘desirability’ function hybrid with a supervised machine learning algorithm was applied to obtain the optimal solutions. The results show a good agreement between actual and predicted values. |
| first_indexed | 2024-12-18T02:47:51Z |
| format | Article |
| id | doaj.art-d011adcb984546389da459b2942e82cd |
| institution | Directory Open Access Journal |
| issn | 0334-8938 2191-0243 |
| language | English |
| last_indexed | 2024-12-18T02:47:51Z |
| publishDate | 2021-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of the Mechanical Behavior of Materials |
| spelling | doaj.art-d011adcb984546389da459b2942e82cd2022-12-21T21:23:31ZengDe GruyterJournal of the Mechanical Behavior of Materials0334-89382191-02432021-09-01301384810.1515/jmbm-2021-0005Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant materialKumar Anish0Sharma Renu1Gupta Arun Kumar2Department of Mechanical Engineering, M. M. deemed to be UniversityMullana-Ambala-133207 (Haryana), IndiaDepartment of Physics, M. M. deemed to be UniversityMullana-Ambala, 133207 (Haryana), IndiaDepartment of Mechanical Engineering, M. M. deemed to be UniversityMullana-Ambala-133207 (Haryana), IndiaCP-Ti G2 has become the preferred biocompatible material for various devices mainly used in orthopedic and dental implants and it is also used in aviation and aircraft. While CP-Ti G2 deals with good ductility, higher stiffness, and fatigue resistance. The novelty of present research work was attentive to the effect of WEDM factors on MRR. After machining, surface topography was examined through SEM. MRR was modeled through ANOVA to analyze the adequacy. It was observed that POT, POFT, PC, and SGV most significant factors. The WEDM factors have also been significantly deteriorating the morphology of machined samples in the form of craters, debris, and micro cracks. A multi-objective optimization ‘desirability’ function hybrid with a supervised machine learning algorithm was applied to obtain the optimal solutions. The results show a good agreement between actual and predicted values.https://doi.org/10.1515/jmbm-2021-0005wedmcp-ti g2biocompatibilitymrrsemsurface morphologydesirability functionmachine learning |
| spellingShingle | Kumar Anish Sharma Renu Gupta Arun Kumar Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material Journal of the Mechanical Behavior of Materials wedm cp-ti g2 biocompatibility mrr sem surface morphology desirability function machine learning |
| title | Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material |
| title_full | Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material |
| title_fullStr | Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material |
| title_full_unstemmed | Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material |
| title_short | Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material |
| title_sort | experimental investigation of wedm process through integrated desirability and machine learning technique on implant material |
| topic | wedm cp-ti g2 biocompatibility mrr sem surface morphology desirability function machine learning |
| url | https://doi.org/10.1515/jmbm-2021-0005 |
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