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Creators/Authors contains: "Ma, Mason"

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  1. ; ; ; ; ; (, Manufacturing Letters)
    Free, publicly-accessible full text available August 1, 2026
  2. ; ; ; ; ; ; (, Manufacturing Letters)
    Free, publicly-accessible full text available August 1, 2026
  3. ; ; ; ; ; (, Manufacturing Letters)
    Full Text Available 
  4. ; ; ; ; (, Journal of Manufacturing Science and Engineering)
    Abstract This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid-state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed AFSD-Nets includes a set of customized neural network approximators, which are used to model the coupled temperature evolution for the tool and build during multi-layer material deposition. Experiments are designed and performed using 7075 aluminum feedstock deposited on a substrate of the same material for 30 layers. A comparison of predictions and measurements shows that the proposed AFSD-Nets approach can accurately describe and predict the temperature evolution during the AFSD process. 
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