TRUST-Based Optimization of High-Performance Machining Process

Abstract

This study aims to optimize aluminum machining parameters for enhanced surface finish, material removal rate, power efficiency, cutting force reduction and thermal stress control. Aluminum's critical role in the automotive and aerospace industries necessitates precise machining to meet high-quality production standards. The optimization was performed using the multi-normalization multi-distance assessment (TRUST) method and Multi-Criteria Decision Analysis (MCDA) approach. Various machining parameters, including spindle speed, feed per tooth, depth of cut and tool diameter, were analyzed. For example, alternative A1 with spindle speed (8000 rpm), feed per tooth (0.6125 mm), depth of cut (1.5 mm) and tool diameter (20 mm) achieved the best surface finish (0.0481 µm) and minimized power requirements (0.2048 kW). Alternative A26 with higher spindle speed (9000 rpm), feed per tooth (0.645 mm) and depth of cut (2 mm) resulted in worse conditions, including higher thermal stress (46634840 N/mm²) and greater cutting force (1702.264 N). The study finds that alternative A1 offers the best performance, with superior surface finish, lower power consumption, reduced cutting forces and minimal thermal stress. In contrast, alternative A26 demonstrates the worst performance in terms of surface quality, power requirement and thermal stress. Validation tests, including surface roughness, cutting force and power consumption measurements, were conducted for both the best and worst conditions to ensure reproducibility and reliability. The study is limited to AA 6061-T6 aluminum and a specific range of machining parameters.