Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
2024-02-24
2024-01-04
2023-11-02
Abstract—Surface roughness is a quality index which partly determines the ability of a material to meet its service or functional requirements. In this study, the Response Surface Methodology (RSM) was used for numerical analysis of the pocket milling operation of AISI D3 alloy steel and this was validated via physical experimentations. The physical experimentations were carried with the aid of a Deckel Maho DMU80mono BLOCK 5-axis CNC milling machine. The range of the process parameters selected include; feed rate between 0.1-0.5 mm/rev, depth of cut between 1- 3 mm and speed of cut between 150-375 m/min. The RSM generated 20 possible experimental runs while their responses (surface roughness) were gotten from physical experimentations. The results of the physical experimentations serve as input into the numerical analysis carried out using the RSM. This was used to obtain a predictive model equation for determining the magnitude of surface roughness as a function of the three cutting parameters employed (feed rate, depth of cut and cutting speed). Furthermore, the optimisation of the solutions obtained generated 10 possible solutions whose desirability values were equal to 1. The findings of this study may assist machinist in achieving good surface quality during the milling operation of AISI D3 alloy steel. Index Terms—optimisation, predictive model equation, process parameters, response surface methodology, surface roughness Cite: Ilesanmi Daniyan, Khumbulani Mpofu, Adefemi Adeodu, and Ikenna Damian Uchegbu, "Numerical and Experimental Analysis of Surface Roughness of AISI D3 Alloy Steel during Pocket Milling Operation," International Journal of Mechanical Engineering and Robotics Research, Vol. 11, No. 10, pp. 793-800, October 2022. DOI: 10.18178/ijmerr.11.10.793-800 Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
