Summary of Afsd-physics: Exploring the Governing Equations Of Temperature Evolution During Additive Friction Stir Deposition by a Human-ai Teaming Approach, By Tony Shi et al.
AFSD-Physics: Exploring the governing equations of temperature evolution during additive friction stir deposition by a human-AI teaming approach
by Tony Shi, Mason Ma, Jiajie Wu, Chase Post, Elijah Charles, Tony Schmitz
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a human-AI collaboration approach to develop a physics-based model for additive friction stir deposition (AFSD), an emerging manufacturing technology that deposits materials without melting. The authors combine first-principles models with AI to create the AFSD-Physics method, which can learn governing equations of temperature evolution from in-process measurements. They validate their model using experiments and demonstrate its accuracy and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines work together with people to make new materials without melting them. The scientists used a special team effort that combined human ideas with machine learning to create a model for this process. They tested the model by measuring what happened during the manufacturing process and found it was very accurate. This could help improve the manufacturing process in the future. |
Keywords
* Artificial intelligence * Machine learning * Temperature
