Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Abstract

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.