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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62578
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mechanical Engineering
RVR&JC Collee Of Engineering
Mechanical Engineering
RVR&JC Collee Of Engineering
Mechanical Engineering
RVR&JC Collee Of Engineering
Mechanical Engineering
RVR&JC Collee Of Engineering
A hybrid experimental and regression modelling approach is developed for multi-objective optimization of CNC turning parameters applied to an Al7075-based Hybrid Metal Matrix Composite (HMMC) reinforced with 1.8 wt.% each of Cu, Co, Cr, Fe, and Al powders under dry cutting conditions. Three machining parameters — spindle speed (800–1600 rpm), feed rate (20–60 mm/min), and depth of cut (0.10–0.50 mm) — were varied using a Taguchi L9 orthogonal array, with surface roughness (SR), cutting temperature (Temp), and material removal rate (MRR) as performance responses. ANOVA confirmed spindle speed as dominant for SR (32.98%), depth of cut for temperature (51.72%) and MRR (72.70%). Entropy weighting yielded objective criterion weights of 0.5932 (MRR), 0.2459 (Temp), and 0.1609 (SR). Both TOPSIS and VIKOR independently identified Experiment 9 (1600 rpm, 60 mm/min, 0.50 mm DoC) as the optimal compromise. Polynomial regression achieved R² = 0.9781 (SR), 0.9156 (Temp), and 1.000 (MRR). Global grid search over 8,000 combinations pinpointed 1600 rpm, 60 mm/min, and 0.10 mm as the continuous-domain optimum. The unified integration of Taguchi + ANOVA + Entropy + TOPSIS + VIKOR + Regression constitutes the principal novelty of this study.
KEYWORDS
(1) Spindle speed dominates SR (32.98%); DoC governs temperature (51.72%) and MRR (72.70%); feed rate is the primary MRR driver (63.67%). (2) Entropy weights of 0.5932 (MRR), 0.2459 (Temp), 0.1609 (SR) were derived objectively without subjective expert input. (3) TOPSIS and VIKOR independently and consistently identified Experiment 9 (1600 rpm, 60 mm/min, 0.50 mm DoC) as the optimal compromise, validating framework robustness via dual-method convergence. (4) Regression R² = 0.9781 (SR), 0.9156 (Temp), 1.000 (MRR) confirm reliable predictive models. (5) Global grid search identified 1600 rpm, 60 mm/min, 0.10 mm as the continuous-domain optimum, extending the discrete L9 results. (6) Principal Novelty: The unified pipeline — Taguchi + ANOVA + Entropy Weighting + TOPSIS + VIKOR + Polynomial Regression — applied to Al7075 HMMC turning constitutes a methodological contribution not previously reported. The framework is directly transferable to other composite machining applications.
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Publication Date
5/15/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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