In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of AA5052 in incremental sheet forming (ISF). The optimization objectives were maximum forming angle and minimum thickness reduction whose values vary in response to changes in production process parameters, such as the tool diameter, step depth, tool feed rate, and tool spindle speed. A Box–Behnken experimental design was used to develop an RSM and BPNN model for modeling the variations in the forming angle and thickness reduction in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process using the GA. The results showed that RSM effectively modeled the forming angle and thickness reduction. Furthermore, the correlation coefficients of the experimental responses and BPNN predictions of the experiment results were good with the minimum value being 0.97936. The Pareto optimal solutions for maximum forming angle and minimum thickness reduction were obtained and reported. The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.

中文翻译：

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AA5052板材增量板材成形过程中的RSM和BPNN建模：使用遗传算法的多目标优化

在这项研究中，使用响应面方法（RSM），反向传播神经网络（BPNN）和遗传算法（GA）对AA5052增量板成形（ISF）的成形参数进行建模和多目标优化。优化目标是最大成型角和最小厚度减小，其值根据生产工艺参数（例如刀具直径，台阶深度，刀具进给速率和刀具主轴速度）的变化而变化。Box-Behnken实验设计用于开发RSM和BPNN模型，以对成形角和厚度减小的变化进行建模，以响应工艺参数的变化。随后，将RSM模型用作适应度函数，以使用GA对ISF过程进行多目标优化。结果表明，RSM有效地模拟了成形角度和厚度减小。此外，实验响应的相关系数和实验结果的BPNN预测均良好，最小值为0.97936。获得并报道了最大成形角度和最小厚度减小的帕累托最优解。由GA生产的经过优化的Pareto前端可以作为AA5052在ISF工艺中实际应用的合理设计指南。