Electromagnetic launching manufacturing (EMLM) is a high-speed material processing technique powered by pulsed current. The hammer velocity is a key indicator in EMLM, but it is hard to obtain. A viable approach is to use the circuit response current to help in measuring the hammer velocity, but the different curve patterns and the weak interactions between the parameters are major hindrances. In this paper, response current information was used to develop a physics-informed neural network to indirectly predict the hammer velocity. With the experimental dataset, the proposed model had the lowest root mean squared error (0.95) compared with the traditional models (1.62 from Seq2Seq, 1.98 from LSTM, and 2.18 from Attention). This improvement was achieved through the proposed physics-informed neural network architecture. The proposed loss function incorporated knowledge from physics which enabled the model to obtain implicit physical data in the EMLM process, thus enhancing its generalization ability. A fast convergence of the training process was promoted by the developed 2-stage strategy. The introduced attention mechanism added a global perspective that improved the prediction accuracy of the peak velocities. Consequently, our model achieved state-of-the-art predictions of velocities in real-world industrial EMLM systems.

中文翻译：

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用于电磁发射制造中速度预测的物理信息神经网络


电磁发射制造（EMLM）是一种由脉冲电流驱动的高速材料加工技术。锤子速度是EMLM中的一个关键指标，但很难获得。一种可行的方法是使用电路响应电流来帮助测量锤子速度，但不同的曲线模式和参数之间的弱相互作用是主要障碍。在本文中，响应电流信息被用来开发物理信息神经网络来间接预测锤子速度。在实验数据集上，与传统模型（Seq2Seq 为 1.62，LSTM 为 1.98，Attention 为 2.18）相比，所提出的模型具有最低的均方根误差（0.95）。这一改进是通过所提出的物理信息神经网络架构实现的。所提出的损失函数结合了物理学知识，使模型能够在 EMLM 过程中获得隐含的物理数据，从而增强其泛化能力。制定的两阶段策略促进了培训过程的快速收敛。引入的注意力机制增加了全局视角，提高了峰值速度的预测准确性。因此，我们的模型实现了现实工业 EMLM 系统中最先进的速度预测。