AFSD-Physics: Exploring the governing equations of temperature evolution
  during additive friction stir deposition by a human-AI teaming approach
Tony Shi Mason Ma Jiajie Wu Chase Post Elijah Charles Tony Schmitz
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.