This paper presents a modeling effort to explore the underlying physics of temperature evolution during additive friction stir deposition (AFSD) by a human-AI teaming approach. AFSD is an emerging solid-state additive manufacturing technology that deposits materials without melting. However, both process modeling and modeling of the AFSD tool are at an early stage. In this paper, a human-AI teaming approach is proposed to combine models based on first principles with AI. The resulting human-informed machine learning method, denoted as AFSD-Physics, can effectively learn the governing equations of temperature evolution at the tool and the build from in-process measurements. Experiments are designed and conducted to collect in-process measurements for the deposition of aluminum 7075 with a total of 30 layers. The acquired governing equations are physically interpretable models with low computational cost and high accuracy. Model predictions show good agreement with the measurements. Experimental validation with new process parameters demonstrates the model's generalizability and potential for use in tool temperature control and process optimization.

该论文介绍了一种建模方法，通过人工智能与人类团队合作的方式，探索了增材摩擦搅拌沉积（AFSD）过程中温度演变的基本物理原理。实验结果表明，该方法可以从过程中获取数据，并生成具有低计算成本和高准确性的物理解释模型，用于工具温度控制和工艺优化。

AFSD-物理学：通过人工智能与人类协作探索附加摩擦搅拌沉积过程中温度演化的控制方程