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Experimental investigation and optimization of process parameters of hybrid Al/SiC/B4C–MMCs finished by MAFM process using RSM modeling with supervised machine learning algorithm

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Abstract

Magnetic abrasive flow machining (MAFM) has an astonishing capability for improving the surface quality of advanced materials viz. composites, ceramics and hard alloys. The surface quality and finishing have major dependency on various process parameters of the focused surface while finishing through MAFM process. The MAFM process procures several applications in medical fields (Knee joint implant and surgical instruments), automotive, aerospace and tool manufacturing industries. The newness of current study is in the development of an MAFM setup for machining of SiC/B4C hybrid MMCs with aluminium-6063 as a base material and the measurement of parametric effects on the process performance. The efforts made have led towards the modeling of two responses viz. MRR and ΔRa with response surface methodology. Box-Behnken design approach has been adopted for analyzing six MAFM factors and a total of 54 trials have been conducted for finding their influence on MRR and ΔRa. SEM and EDX have been applied to examine the surface topography. The significance of various process parameters has been analyzed by using ANOVA. The outcomes showed that Ep (extrusion pressure), M (mesh size), N (number of cycles), and Mf (magnetic flux density) are the most essential factors. The optimal solutions have been attained by applying a multi-objective optimization ‘desirability’ function using statistical and supervised machine learning algorithms which led to the parametric machine learning algorithms reflection for surmising the efficiency of MAFM process. A fine consonance has been obtained among the predicted and actual values. The graphical abstract of the current research work is shown below.

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Abbreviations

AR2 :

Adjusted R2

AP:

Adequate precision

CI:

Confidence interval

ANOVA:

Analysis of variance

LOF:

Lack of fit

BBD:

Box–Behnken design

Ep :

Extrusion pressure

N:

Number of cycles

Al/SiC/B4C:

Aluminium-6063/Silicon carbide/Boron carbide

C:

Concentration of abrasives

MMCs:

Metal matrix composites

EDX:

Energy dispersive X-ray analysis

MAFM:

Magnetic abrasive flow machining

MS:

Mean square

MRR:

Material removal rate

PR2 :

Predicted R2

ΔRa :

Change in surface roughness

RSM:

Response surface methodology

Wp :

Workpiece material

M:

Mesh size

Mf :

Magnetic flux density

SEM:

Scanning electron microscope

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Acknowledgements

The authors would like to thank the National Institute of Technology (NIT), Kurukshetra, Haryana, India, to provide the essential facilities for current research work. The authors are also thankful to Thapar University Patiala, Punjab, India for granting the permission of using the resources in their laboratory (SEM and EDX). No financial assistance has been received from any institution or agency.

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  1. Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Gagandeep Chawla & Vinod Kumar Mittal

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Chawla, G., Mittal, V.K. Experimental investigation and optimization of process parameters of hybrid Al/SiC/B4C–MMCs finished by MAFM process using RSM modeling with supervised machine learning algorithm. Sādhanā 48, 73 (2023). https://doi.org/10.1007/s12046-023-02106-2

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