论文标题
通过人工神经网络对peen形成模式的有效计划
Efficient planning of peen-forming patterns via artificial neural networks
论文作者
论文摘要
弹药形成过程的强大自动化需要闭环反馈，在这种反馈中，对于每种治疗迭代，都需要实时找到合适的治疗模式。在这项工作中，我们提出了一种基于神经网络（NN）查找PEEN形成模式的方法，该方法从有限元仿真生成的数据中学习了将给定目标形状（输入）与其最佳佩切模式（输出）相关联的非线性函数。训练有素的NN产生模式，平均二进制精度为98.8 \％，相对于微秒的地面真相。
Robust automation of the shot peen forming process demands a closed-loop feedback in which a suitable treatment pattern needs to be found in real-time for each treatment iteration. In this work, we present a method for finding the peen-forming patterns, based on a neural network (NN), which learns the nonlinear function that relates a given target shape (input) to its optimal peening pattern (output), from data generated by finite element simulations. The trained NN yields patterns with an average binary accuracy of 98.8\% with respect to the ground truth in microseconds.
