Abstract An integrated model based on generalized regression neural network (GRNN) and non-dominated sorting genetic algorithm (NSGAII) with elite strategy is proposed to predict and optimize the quality characteristics of fiber laser cutting stainless steel. An orthogonal experiment has been conducted where laser power, cutting speed, gas pressure, defocus are considered as controllable input parameters with kerf width and surface roughness as output to generate the dataset for the model. In GRNN-NSGAII model, the cross-validation method was performed to train the network to obtain the optimal GRNN. Significance of controllable parameters of laser on outputs is also discussed. The GRNN model is determined as the fitness function for prediction and calculation during the NSGAII optimization process. NSGAII generates complete optimal solution set with Pareto optimal front for outputs. The prediction relative error of GRNN model is within ±5%. Experimental verification error of optimized output less than 5%. Characterization of the process parameters in Pareto optimal region has been described in detail.

中文翻译：

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基于广义回归神经网络和非支配排序遗传算法的光纤激光切割多目标优化

摘要 提出了一种基于广义回归神经网络（GRNN）和非支配排序遗传算法（NSGAII）的精英策略集成模型来预测和优化光纤激光切割不锈钢的质量特性。进行了正交实验，其中激光功率、切割速度、气压、散焦被视为可控输入参数，切口宽度和表面粗糙度作为输出，以生成模型的数据集。在 GRNN-NSGAII 模型中，通过交叉验证的方法来训练网络以获得最优的 GRNN。还讨论了激光器的可控参数对输出的意义。GRNN 模型被确定为 NSGAII 优化过程中进行预测和计算的适应度函数。NSGAII 生成带有 Pareto 最优输出的完整最优解集。GRNN模型的预测相对误差在±5%以内。优化输出的实验验证误差小于5%。已详细描述了帕累托最优区域中工艺参数的表征。