标题： 基于物理信息的机器学习代理模型用于线弧定向能量沉积过程中热历史的可扩展模拟 显示英文标题

标题： Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition

摘要： 线弧定向能量沉积（DED）已成为一种有前景的增材制造（AM）技术，适用于大型结构工程应用。 然而，该过程固有的复杂热力学动态在确保所制造的厚壁和板件的结构完整性和机械性能方面带来了挑战。 尽管有限元方法（FEM）模拟通常被用于预测沉积过程中的热历史，但其计算需求对于实际的大规模应用而言仍然过高。 鉴于热管理以及确定最佳打印策略需要进行多次重复模拟，FEM模拟很快变得完全不可行。 相反，已取得进展的是使用训练好的神经网络作为代理模型以实现快速预测。 然而，传统的数据驱动方法在神经网络的训练和验证过程中需要大量相关且可验证的外部数据。 对于大规模线弧 DED 来说，这些数据源都无法以足够的数量获得，从而无法准确建立代理模型。 物理信息神经网络（PINNs）的引入通过结合现有现象的物理知识与先进的机器学习方法，开辟了一种替代的模拟策略。 尽管它们在理论上具有优势，但在结构工程的大规模线弧 DED 背景下，PINNs 的应用仍较为有限。 本研究调查了 PINNs 的可扩展性，重点在于高效的配点采样，这是控制训练时间和模型性能的关键因素。 结果表明，PINNs 可将计算时间和工作量减少高达 98.6%，同时保持所需的准确性并提供“超分辨率”。 还讨论了未来提升金属 AM 中 PINN 性能的方向。 显示英文摘要

摘要： Wire-arc directed energy deposition (DED) has emerged as a promising additive manufacturing (AM) technology for large-scale structural engineering applications. However, the complex thermal dynamics inherent to the process present challenges in ensuring structural integrity and mechanical properties of fabricated thick walls and plates. While finite element method (FEM) simulations have been conventionally employed to predict thermal history during deposition, their computational demand remains prohibitively high for actual large-scale applications. Given the necessity of multiple repetitive simulations for heat management and the determination of an optimal printing strategy, FEM simulation quickly becomes entirely infeasible. Instead, advancements have been made in using trained neural networks as surrogate models for rapid prediction. However, traditional data-driven approaches necessitate large amounts of relevant and verifiable external data, during the training and validation of the neural network. Regarding large-scale wire-arc DED, none of these data sources are readily available in quantities sufficient for an accurate surrogate. The introduction of physics-informed neural networks (PINNs) has opened up an alternative simulation strategy by leveraging the existing physical knowledge of the phenomena with advanced machine learning methods. Despite their theoretical advantages, PINNs have seen limited application in the context of large-scale wire-arc DED for structural engineering. This study investigates the scalability of PINNs, focusing on efficient collocation points sampling, a critical factor controlling both the training time and model performance. Results show PINNs can reduce computational time and effort by up to 98.6%, while maintaining the desired accuracy and offering "super-resolution". Future directions for enhancing PINN performance in metal AM are discussed.