Multi-Objective Optimization (MOO) of Machining Process Factors by Hybrid Taguchi Grey Relational Analysis (HGRA)

International Journal of Mechanical Engineering

© 2025 by SSRG - IJME Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Prashant D. Kamble, Shilpa B. Sahare, Heena D. Mehta

10.14445/23488360/IJME-V12I3P109

How to Cite?

Prashant D. Kamble, Shilpa B. Sahare, Heena D. Mehta, "Multi-Objective Optimization (MOO) of Machining Process Factors by Hybrid Taguchi Grey Relational Analysis (HGRA)," SSRG International Journal of Mechanical Engineering, vol. 12,  no. 3, pp. 109-115, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I3P109

Abstract:

An effort to optimize machining processes, such as turning operations, is made in this work. The following are adjustable process parameters: Cutting Environment (CE), Feed Rate (FR), Depth Of Cut (DOC), Nose Radius (NR), and Type of Tool (TT). The parameters of the response are Surface Roughness (SR), Material Removal Rate (MRR), Cutting Force (CF), Tool tip Temperature (Temp) and Tool Wear (TW). For experiments, the L27 Taguchi (OA) Orthogonal Array is employed. The CNC Spinner lathe was used for the experiment. The most recent coolant system, MQL, is employed in today's manufacturing setting. Taguchi method and Grey Relational Analysis (TGRA) are utilized for data analysis. GRA processes several output parameters and combines them into a single response parameter. Lastly, the ideal process parameters are determined using the traditional Taguchi approach. The contribution and ranking of input parameters are determined using ANOVA and Signal-to-Noise Ratio (SRN) in order to maximize the number of output parameters. The conformance test is used to formulate the results' hypothesis. The conformance test findings showed that both the experimental and projected values are acceptable.

Keywords:

Taguchi method, Turning process, Multi-optimization, Grey Relational Analysis.

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