Exploring cryo-MQL medium for hard machining of hastelloy C276: a multi-objective optimization approach

Hastelloy C276 alloys are renowned for their exceptional tool-work interaction, high-temperature strength, and toughness. Despite their widespread use, machining these alloys poses challenges such as tool wear and elevated temperatures at the tool-work interface. To address these issues, modern lubrication/cooling methods like Minimum Quantity Lubrication (MQL), Cryogenic cooling, and hybrid Cryogenic-MQL (Cryo-MQL) have emerged as effective options for machining heat-resistant alloys. This study investigates the comparative effectiveness of these methods against dry machining and optimizes milling parameters through Multi-objective Particle Swarm Optimization (MOPSO) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The optimization aims to minimize energy consumption, machining cost, and surface roughness. Utilizing response surface equations, MOPSO identifies optimal solutions, and TOPSIS selects the most favorable solution among them. Experimental results validate the robustness of the multi-attribute optimization model, demonstrating a 5% average error between predicted and experimental responses. This research provides valuable insights into enhancing the machining quality of Hastelloy C276 while minimizing energy and cost consumption.