Summary of Physics-informed Machine Learning For Smart Additive Manufacturing, by Rahul Sharma et al.
Physics-Informed Machine Learning for Smart Additive Manufacturing
by Rahul Sharma, Maziar Raissi, Y.B. Guo
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to smart manufacturing is proposed by developing a physics-informed machine learning (PIML) model that integrates neural networks with physical laws. This PIML model aims to improve the accuracy, transparency, and generalization of data-driven machine learning models, which often struggle with interpretability. The authors achieve this by leveraging the strengths of both machine learning and physical laws in advanced manufacturing processes such as laser metal deposition (LMD). By combining the representational power of neural networks with the governing principles of physical systems, the PIML model demonstrates improved performance on LMD tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way to make things that’s more precise and efficient. Right now, computers are used to control manufacturing processes, but these “black boxes” can be hard to understand. This paper tries to solve this problem by creating a special kind of computer program that uses both math rules and machine learning techniques. The goal is to make the program better at predicting what will happen during manufacturing processes like 3D printing with lasers. By combining the strengths of these different approaches, the authors hope to create a more accurate and transparent way to make things. |
Keywords
» Artificial intelligence » Generalization » Machine learning
