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

Area of Science:

• Manufacturing Engineering
• Materials Science
• Optimization Techniques

Background:

• Optimizing machining parameters is vital for improving machinability and product quality.
• Existing research often lacks comprehensive comparisons of various Multi-Criteria Decision Making (MCDM) techniques for machining optimization.

Purpose of the Study:

• To evaluate and compare five Taguchi-based MCDM techniques (CoCoSo, GRA, MOORA, TOPSIS, COPRAS) combined with the Entropy method.
• To optimize machining parameters (feed rate, cutting speed) for medium carbon steel under dry and Minimum Quantity Lubrication (MQL) conditions.
• To assess the impact of MQL on critical machining responses including material removal rate, surface roughness, cutting force, temperature, ratio, and tool life.

Main Methods:

• Application of five 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).
• Integration with the Entropy method for objective weighting of machining responses.
• Experimental validation and analysis using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX).

Main Results:

• Minimum Quantity Lubrication (MQL) consistently enhanced machining performance across most responses.
• COPRAS, TOPSIS, MOORA, and GRA identified similar optimal parameters: MQL environment, 0.14 mm/rev feed rate, and 137 m/min cutting speed.
• CoCoSo and GRA exhibited the highest reliability with minimal prediction errors (0.647% and 0.659%, respectively). COPRAS showed strong predictive accuracy (5.573% error).
• SEM/EDX analyses confirmed reduced tool wear and improved surface quality under MQL conditions.

Conclusions:

• MQL is a superior machining strategy for medium carbon steel, offering significant improvements in machinability and tool life.
• COPRAS is a reliable MCDM technique for machining parameter optimization, potentially replacing TOPSIS and MOORA in similar applications.
• The study provides valuable insights for sustainable manufacturing through effective parameter optimization and MQL adoption.