ANALYSIS OF THE THERMAL FRICTION DRILLING PROCESS FOR EFFECTIVE PARAMETRIC CHOICE ON THE DRILLING OF AISI 304 STAINLESS STEEL USING THE FUZZY AHP-MOORA METHOD

Sunday Ayoola Oke; Ugochukwu Sixtus Nwankiti; John Rajan; Swaminathan Jose; Samuel Bolaji Aderibigbe; Samson Oluwaseun Odudare

doi:10.30572/2018/KJE/150411

Authors

Sunday Ayoola Oke University of Lagos, Lagos, Nigeria https://orcid.org/0000-0002-0914-8146
USN University of Lagos, Lagos, Nigeria
JR Vellore Institute of Technology, Vellore, India
SJ Vellore Institute of Technology, Vellore, India
SBA University of Lagos, Lagos, Nigeria
SOO University of Lagos, Lagos, Nigeria

DOI:

https://doi.org/10.30572/2018/KJE/150411

Keywords:

Friction;, drilling;, axial force;, feed rate;, rotational speed;, workpiece;, bushing length;

Abstract

Attaining effective quality control of drilled samples in thermal friction drilling is a challenge considering the disproportionate allocation of drilling resources to parameters. Therefore, it is essential to select the appropriate parameters and allocate their scarce resources based on requirements. This paper chooses the effective and the best parameters of the drilling process during the processing of AISI 304 stainless steel using the fuzzy-AHP-MOORA method. The analytic hierarchy process is deployed by stating the criteria and alternatives. A pairwise comparison is made with the outcome introduced into a fuzzy framework which interpretes the obtained values from an input to an output vector via a rule (linguistic the terms) set. The result is expressed as responses to options compared to the objectives as ratios. The input parameters are the feed rate, friction angle, rotational speed, friction contact area proportion, tool cylindrical region diameter and workpiece thickness. In turn, the responses are the roundness error, radial force, dimensional error, axial force, bushing length and hole diameter. It was found that experimental trials 12, 14 and 8 with the respective differences between beneficial and non-beneficial values of 0.2133, 0.2076 and 0.1083 obtained the respective positions of 1st, 2nd and 3rd. The discretized fuzzy weights place the bushing length as the best while the second position is shared equally by all the other responses. The model was successful in reducing the imprecision in the parameters and the greatly improved response was the bushing length.

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