本研究探讨了加工参数如何影响表面粗糙度(SR),刀具磨损率(TWR),混合铝金属基复合材料(AMMC)的电火花加工(EDM)过程中的材料去除率(MRR)。该复合材料包括在铝7075(Al7075)基体中的6%碳化硅(SiC)和6%碳化硼(B4C)。采用组合优化方法来平衡这些因素,评估脉冲开启时间,Current,电压,和脉冲关闭时间。响应面方法(RSM)优化的单个响应,而多响应优化采用了结合熵权法(EWM)的混合方法,田口方法,TOPSIS,和GRA。方差分析(ANOVA)评估参数显著性,揭示了对SR的重大影响,MRR,和EWR。基于TOPSIS和GRA,优化的参数达到了理想的平衡:高MRR(0.4172,0.5240毫米/分钟),最小EWR(0.0068,0.0103mm2001/min),和基于EWM加权优先级的可接受SR(10.3877,9.1924μm)。确认实验验证了接近系数提高了15%,灰色关联度提高了16%,考虑组合SR,MRR,和EWR性能。用最佳参数加工的表面的扫描电子显微镜(SEM)分析显示出最少的碎片,裂缝,没有重铸层,表明高表面完整性。这项研究增强了AMMC的EDM优化,实现加工效率,最小化工具磨损,满足表面质量要求。
This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (
ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 μm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.