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Investigation of biocompatible implant material through WEDM process using RSM modeling hybrid with the machine learning algorithm

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Abstract

CP-Ti-G2 (Commercially pure titanium grade-2) 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 deals with good ductility, higher stiffness, and fatigue resistance. The novelty of present research work was to create a rough surface on CP-Ti-G2 through the WEDM process. Further, this rough surface was used in the development of bone marrow cells on it. Propagation and diversity of bone marrow cells were applied in dental implant osteointegration applications. Six WEDM factors were analyzed through the BBD design of the experiment. 54 trial experiments were conducted to observe the MRR and SR output responses. After machining, surface topography was examined through SEM and EDX. ANOVA was applied to analyze the significance of factors. It was observed that POT (pulse on time), POFT (pulse off time), PC (peak current), and SGV (spark gap voltage) are the most significant factors. The WEDM factors have also been significantly deteriorating the microstructure of machined samples remarkably deeper, wider craters, globules of debris, and micro cracks. A multi-objective optimization ‘desirability’ function was applied to obtain the optimal solutions by numerical and supervised machine learning algorithms. They lead to the reflection of parametric machine learning algorithms to surmise about the effectiveness of WEDM process. The results show a good agreement between actual and predicted values.

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Abbreviations

AR2 :

Adjusted R2

AP:

Adequate Precision

ANOVA:

Analysis of variance

BBD:

Box-Behnken design

CP-Ti-G2:

Commercially pure titanium grade-2

CI:

Confidence interval

CNC:

Computer numerical control

EDX:

Energy dispersive X-ray analysis

LOF:

Lack of fit

MS:

Mean square

MRR:

Material removal rate

POT:

Pulse on time

POFT:

Pulse off time

PC:

Peak current

PE:

Pure error

PR2 :

Predicted R2

RSM:

Response surface methodology

SR:

Surface roughness

SEM:

Scanning electron microscope

SGV:

Spark gap voltage

SS:

Sum of square

WEDM:

Wire electric discharge machining

WS:

Wire speed

WT:

Wire tension

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Acknowledgements

The authors would like to thank the central tool room, Ludhiana, Punjab, India, for providing the necessary WEDM set-up for experimentation. The authors are also thankful to IIT, Ropar, M.M College of Dental Sciences Mullana, Ambala, India for the permission to use their laboratory facilities (SEM, XRD, and EDX). No funding was received from any agency or institution.

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Authors and Affiliations

  1. Department of Mechanical Engineering, M. M. Deemed to be University, Mullana-Ambala, Haryana, 133 207, India

    Anish Kumar & Arun Kumar Gupta

  2. Department of Physics, M. M. Deemed to be University, Mullana-Ambala, Haryana, 133 207, India

    Renu Sharma

  3. Department of Computer Science, M. M. Deemed to be University, Mullana-Ambala, Haryana, 133 207, India

    Rajneesh Gujral

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  1. Anish Kumar
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Correspondence to Anish Kumar.

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Kumar, A., Sharma, R., Gupta, A.K. et al. Investigation of biocompatible implant material through WEDM process using RSM modeling hybrid with the machine learning algorithm. Sādhanā 46, 148 (2021). https://doi.org/10.1007/s12046-021-01676-3

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  • DOI: https://doi.org/10.1007/s12046-021-01676-3

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