Experimental Study on Multi-objective Optimization of EDM Small Hole Machining for TC4 Titanium Alloy
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摘要: 为提升电火花加工TC4钛合金的表面加工质量和加工效率, 选取紫铜圆柱电极开展TC4钛合金电火花小孔加工试验, 采用正交试验法, 以电极相对损耗率、表面粗糙度、工件材料去除体积为工艺指标, 分析峰值电流、维持电压、放电脉宽对工艺指标的影响重要性。采用RBF(Radial basis function)神经网络对已有试验数据进行训练, 建立放电参数与工艺指标之间的数学预测模型。以该预测模型为适应度函数, 将遗传算法与Skyline选择算法结合进行多目标优化仿真, 得到最佳工艺指标, 最后开展多目标优化验证试验。结果表明: 当峰值电流为14 A、维持电压39 V/42 V、放电脉宽102 μs/108 μs时能够取得最优的加工结果, 优化值与试验值误差较小。
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关键词:
- 电火花加工 /
- RBF神经网络 /
- 遗传算法 /
- Skyline选择算法 /
- 多目标优化
Abstract: To improve the surface machining quality and machining efficiency of TC4 titanium alloy in EDM (Electrical discharge machining), the copper cylindrical electrode was selected to carry out EDM small hole machining experiment of TC4 titanium alloy. The orthogonal experiment method was adopted. Taking the relative electrode wear rate, surface roughness and material removal volume of workpiece as optimization objectives, the influences of the peak current, discharge voltage and discharge pulse width on the optimization objectives were analyzed. RBF (Radial basis function) neural network was used to train with the experimental data, and the prediction model between the discharge parameters and the optimization objectives was established. Taking the prediction model as the fitness function, the multi-objective optimization simulation was carried out by combining the genetic algorithm with the Skyline selection algorithm, and the optimal technical index was obtained. Finally, the multi-objective optimization verification experiment was carried out. The results show that when the peak current is 14 A, the maintenance voltage is 39 V/42 V, and the discharge pulse width is 102 μs/108 μs, the optimal machining results can be obtained, and the error between the optimal value and the experimental value is small. -
表 3 放电加工正交试验结果
序号 峰值电流/A 维持电压/V 放电脉宽/μs 电极相对损耗率/% 表面粗糙度/μm 工件材料去除体积/mm3 1 14 30 50 9.321 1.955 5.987 2 14 40 150 7.078 3.795 7.095 3 14 50 100 10.786 3.121 6.208 4 18 30 150 7.366 3.164 9.091 5 18 40 100 19.093 2.725 6.430 6 18 50 50 19.246 2.721 7.539 7 22 30 100 16.016 2.573 4.878 8 22 40 50 16.778 2.761 10.643 9 22 50 150 16.237 2.988 6.874 K1 9.060 10.901 15.115 K2 15.235 14.316 15.298 K3 16.344 15.423 10.227 极差R1 7.284 4.522 5.071 K4 2.957 2.564 2.479 K5 2.870 3.094 2.806 K6 2.774 2.943 3.316 极差R2 0.183 0.151 0.837 K7 6.430 6.652 8.056 K8 7.687 8.056 5.839 K9 7.465 6.874 7.687 极差R3 1.257 1.404 0.369 
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表 4 神经网络模型试验值与预测值对比
序号 峰值电流/A 放电电压/V 放电脉宽/μs 电极相对损耗率/% 表面粗糙度/μm 工件材料去除体积/mm3 试验值 预测值 试验值 预测值 试验值 预测值 1 14 30 100 9.7 10.9 2.462 2.409 5.765 5.085 2 14 30 150 7.5 8.8 2.665 2.608 5.987 5.252 3 14 50 150 5.2 4.8 5.213 4.964 6.430 6.153 4 22 30 150 17.2 15.5 4.367 4.005 8.426 8.911
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表 5 多目标优化结果与试验值对比
序号 峰值电流/A 放电电压/V 放电脉宽/μs 电极相对损耗率/% 表面粗糙度/μm 工件材料去除体积/mm3 试验值 预测值 试验值 预测值 试验值 预测值 1 14 39 102 5.761 6.742 1.985 2.236 5.024 4.845 2 14 42 108 6.453 7.231 1.982 2.334 4.847 4.213
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