线切割加工中多目标优化算法的应用

摘要

为了解决慢走丝线切割加工中难以同时获得较快加工速度和较优表面质量的问题,从其加工参数与加工指标之间的高度非线性关系入手;选取水压(WP)、脉冲时间(Ton)、脉冲间隔(Toff)、电极丝张力(WT)、丝速(WS)和伺服参考电压(SV)作为优化参数,以表面粗糙度(Ra)、材料去除率(MRR)作为优化指标,设计正交实验;创新运用支持向量机回归(SVMR)结合粒子群优化算法(PSO)建立其多目标预测优化模型,得到最优加工参数;实验结果表明,所建立的多目标预测优化模型预测精度高、优化效果显著.%To simultaneously achieve a higher material removal rate and a better surface integrity,the highly nonlinear relationship between the process parameters and the machining performance was investigated.A Taguchi experiment was designed with water pressure (WP),pulse-on time (Ton),pulse-off time (Toff),wire tension (WT),wire speed (WS) and servo voltage (SV) as the main optimization parameters,surface roughness (Ra) and material removal rate (MRR) as the optimization targets.It is innovative to apply the support vector machines regression (SVMR) and particle swarm algorithm (PSO) to acquire the optimized parameters combination by establishing a multi-objective model.The verification experiment showed that the multi-objective optimization model is efficient for increasing MRR and reducing Ra.