Multi response optimization in MQL milling of GTD450 stainless steel using an integrated Taguchi grey relational analysis

采用集成田口灰色关联分析法对GTD450不锈钢MQL铣削进行多目标优化

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Abstract

The GTD450 stainless steel is highly valued in aerospace and turbine applications for its exceptional mechanical and thermal properties; however, its low machinability and the scarcity of data on optimal cutting and lubrication conditions present a significant industrial challenge. While the environmentally friendly Minimum Quantity Lubrication (MQL) method is a promising candidate for machining this alloy, a comprehensive study on the application and optimization of its key parameters-specifically oil concentration and spray pressure, in conjunction with standard cutting variables-was absent. To address this research gap, this study employed a Taguchi L25 orthogonal array to experimentally investigate the simultaneous effects of these parameters. The results demonstrated that fluid concentration improved surface roughness by up to 12%, while MQL pressure reduced tool wear by up to 37.5%. Furthermore, multi-objective optimization using the Grey Relational Grade (GRG) method yielded a 38.8% improvement in the overall performance index, identifying the optimal parameter set as follows: 15% concentration, 900 mm/min feed rate, 180 m/min cutting speed, 1.5 mm depth of cut, and 10 bar pressure. These significant findings are now being successfully implemented in relevant manufacturing sectors.

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