基于并联GABP与NSGA-Ⅱ的微铣削子午线轮胎模具侧板工艺参数优化

Optimization of the micro-milling radial tire mold side plateprocess parameters based on parallel GABP and NSGA-Ⅱ

针对微铣削子午线轮胎模具侧板过程中存在刀具磨损严重和能量消耗高的问题,提出了一种基于并联GABP神经网络和NSGA-Ⅱ的多目标工艺参数优化方法。对传统多目标GABP预测模型进行了改进,以试验数据为样本建立了切削三要素为输入,刀具磨损面积与切削比能为输出的并联GABP神经网络预测模型,刀具磨损面积预测误差降低了40.82%。以最小刀具磨损面积、最小切削比能为优化目标,利用NSGA-Ⅱ遗传算法对切削参数进行多目标优化,获得了20组pateto解。最终在兼顾刀具磨损面积和切削比能的情况下,通过对原始试验数据和pareto解集进行灰色关联分析获得了最优切削参数组合:n=19185.423 r/min,f z=0.038 mm/z,a p=0.517 mm,实现了工艺参数优化。

In view of the micro-milling tool wear exist in the process of radial tire mold side plate and the problem of high energy consumption,this paper proposes a parallel GABP neural network and the NSGA-Ⅱmultiobjective process parameters optimization met-hod.The traditional multi-target GABP prediction model was improved,and the parallel GABP neural network prediction model with three cutting elements as input,and tool wear and specific cutting energy as output was established with the experimental data as the sample.The prediction error of tool wear was reduced by 40.82%.With the minimum amount of tool wear,the minimum specific cutting energy than for the optimization goal,using the NSGA-Ⅱgenetic algorithm to multiobjective optimization of cutting parameters,20 pateto solution were obtained.Finally,under the condition of both tool wear and specific cutting energy,the optimal cutting parameter combination was obtained through grey correlation analysis of original test data and pareto solution set:n=19185.423 r/min,f z=0.038 mm/z,a p=0.517 mm,realizing process parameter optimization.