数控加工工艺参数的多目标优化与决策算法研究

Multi-object optimization and decision algorithm of numerical control processing parameters

为了提高汽油机活塞数控车削加工的质量和生产效率,提出了基于非支配排序差分进化算法的多目标优化方法与基于加权相对距离的决策方法。以加工件的表面粗糙度和材料去除速率为优化参数,建立了多目标优化模型。鉴于表面粗糙度的经验公式计算精度有限,且不具有生产设备和生产过程差异适应性,给出了学习因子自适应神经网络的拟合方法。使用非支配排序差分进化算法对多目标优化模型进行求解,得到了Pareto前沿解集。提出了基于加权相对距离的决策方法,得到了最优生产方案。经验证,与工厂现用生产方案比,优化后的工件表面粗糙度减小了一倍以上,材料去除速率提高了57.98%,以上数据充分证明了优化方案的有效性。

In order to improve the quality and production efficiency of NC turning of gasoline engine piston,a multi-objective optimization method based on non-dominated sorting differential evolution algorithm and a decision-making method based on weighted relative distance are proposed.Taking the surface roughness and material removal rate as the optimization parameters,a multi-objective optimization model was established.In view of the limited accuracy of the empirical formula of surface roughness and the unadaptability of different production equipment and process,the fitting method of learning factor adaptive neural network is proposed.The non-dominated sorting differential evolution algorithm is used to solve the multi-objective optimization model,and the Pareto frontier solution set is obtained.A decision-making method based on weighted relative distance is proposed,and the optimal production plan is obtained.After verification,compared with the current production plan,the surface roughness of the optimized workpiece is reduced by more than twice,and the material removal rate is increased by 57.98%.The above data fully prove the effectiveness of the optimization plan.