Advanced Manufacturing (AM) has gained significant interest in the nuclear community for its potential application on nuclear materials. One challenge is to obtain desired material properties via controlling the manufacturing process during runtime. Intelligent AM based on deep reinforcement learning (DRL) relies on an automated process-level control mechanism to generate optimal design variables and adaptive system settings for improved end-product properties. A high-fidelity thermo-mechanical model for direct energy deposition has recently been developed within the MOOSE framework at the Idaho National Laboratory (INL). The goal of this work is to develop an accurate and fast-running reduced order model (ROM) for this MOOSE-based AM model that can be used in a DRL-based process control and optimization method. Operator learning (OL)-based methods will be employed due to their capability to learn a family of differential equations, in this work, produced by changing process variables in the Gaussian point heat source for the laser. We will develop OL-based ROM using Fourier neural operator, and perform a benchmark comparison of its performance with a conventional deep neural network-based ROM.

翻译：先进制造（AM）在核材料领域的潜在应用引起了核界广泛关注。其中一个挑战是通过在运行过程中控制制造工艺来获得所需的材料特性。基于深度强化学习（DRL）的智能先进制造依赖于自动化的工艺级控制机制，以生成最优设计变量和自适应系统设置，从而改进最终产品的性能。爱达荷国家实验室（INL）近期在MOOSE框架内开发了一个用于直接能量沉积的高保真热力学模型。本工作旨在为这个基于MOOSE的AM模型开发一个精确且快速运行的降阶模型（ROM），使其可用于基于DRL的工艺控制与优化方法。我们将采用基于算子学习（OL）的方法，因其能够学习由高斯点热源中激光工艺参数变化产生的微分方程组系列。我们将利用傅里叶神经算子开发基于OL的ROM，并将其性能与传统深度神经网络ROM进行基准对比。