Combining RSM, Pareto Analysis, and EDAS for Multi-Objective Optimization of Turning Performance
Received: 9 February 2026 | Revised: 13 April 2026 | Accepted: 25 April 2026 | Online: 14 May 2026
Corresponding author: Ngoc Linh Pham
Abstract
This study investigates the effects of cutting speed (Vc), feed rate (fz), and depth of cut (ap) on surface roughness (Ra) and Material Removal Rate (MRR) in turning operations. Fifteen experiments were conducted with Vc ranging from 100 to 180 m/min, fz from 0.050 to 0.120 mm/rev, and ap from 0.050 to 0.200 mm. Analysis of Variance (ANOVA) revealed that fz is the most influential parameter on Ra, accounting for 49.69% of the total variation, while Vc and ap show no statistically significant effects at the 95% confidence level. In contrast, MRR is significantly affected by all cutting parameters, with depth of cut contributing the most (54.27%), followed by fz (25.56%) and Vc (12.31%). Logarithmic regression models were developed to describe the responses, yielding an adjusted coefficient of determination of 0.655 for Ra and 1.000 for MRR. A multi-objective optimization problem was formulated to minimize Ra and maximize MRR, and an extended Pareto front comprising nine non-dominated solutions was obtained. The Pareto solutions were ranked using the Evaluation based on Distance from Average Solution (EDAS) method, yielding the optimal compromise solution at a Vc value of 180 m/min, fz equal to 0.085 mm/rev, and ap equal to 0.200 mm, with an Ra value of 0.525 µm and an MRR value of 3060 mm³/min. The proposed framework effectively balances surface quality and productivity in turning operations.
Keywords:
turning optimization, surface roughness, material removal rate, response surface methodology, pareto optimization, EDAS, multi-objective optimization, ANOVADownloads
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Copyright (c) 2026 Nhat Tan Nguyen, Anh Thang Nguyen, Ngoc Linh Pham
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