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...

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Autores principales:	Kumar Anish, Sharma Renu, Gupta Arun Kumar
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	wedm cp-ti g2 biocompatibility mrr sem surface morphology desirability function machine learning Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/d011adcb984546389da459b2942e82cd
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spelling	oai:doaj.org-article:d011adcb984546389da459b2942e82cd2021-12-05T14:10:52ZExperimental investigation of WEDM process through integrated desirability and machine learning technique on implant material0334-89382191-024310.1515/jmbm-2021-0005https://doaj.org/article/d011adcb984546389da459b2942e82cd2021-09-01T00:00:00Zhttps://doi.org/10.1515/jmbm-2021-0005https://doaj.org/toc/0334-8938https://doaj.org/toc/2191-0243CP-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.Kumar AnishSharma RenuGupta Arun KumarDe Gruyterarticlewedmcp-ti g2biocompatibilitymrrsemsurface morphologydesirability functionmachine learningMechanical engineering and machineryTJ1-1570ENJournal of the Mechanical Behavior of Materials, Vol 30, Iss 1, Pp 38-48 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wedm cp-ti g2 biocompatibility mrr sem surface morphology desirability function machine learning Mechanical engineering and machinery TJ1-1570
spellingShingle	wedm cp-ti g2 biocompatibility mrr sem surface morphology desirability function machine learning Mechanical engineering and machinery TJ1-1570 Kumar Anish Sharma Renu Gupta Arun Kumar Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material
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.
format	article
author	Kumar Anish Sharma Renu Gupta Arun Kumar
author_facet	Kumar Anish Sharma Renu Gupta Arun Kumar
author_sort	Kumar Anish
title	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_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_sort	experimental investigation of wedm process through integrated desirability and machine learning technique on implant material
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/d011adcb984546389da459b2942e82cd
work_keys_str_mv	AT kumaranish experimentalinvestigationofwedmprocessthroughintegrateddesirabilityandmachinelearningtechniqueonimplantmaterial AT sharmarenu experimentalinvestigationofwedmprocessthroughintegrateddesirabilityandmachinelearningtechniqueonimplantmaterial AT guptaarunkumar experimentalinvestigationofwedmprocessthroughintegrateddesirabilityandmachinelearningtechniqueonimplantmaterial
_version_	1718371654085640192

Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material

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