Summary of Thermal-mechanical Physics Informed Deep Learning For Fast Prediction Of Thermal Stress Evolution in Laser Metal Deposition, by R. Sharma and Y.b. Guo
Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition
by R. Sharma, Y.B. Guo
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This study introduces a novel machine learning framework for predicting thermal stress evolution in metal additive manufacturing. The proposed approach, called physics-informed neural network (PINN), combines deep neural networks with governing physical laws to accurately predict temperature and thermal stress during the laser metal deposition process. By incorporating physical laws into the model, PINNs can achieve accurate predictions using small amounts of simulation data, making them more efficient than traditional machine learning models that require large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of artificial intelligence called physics-informed neural networks to predict how hot metal gets when it’s being made layer by layer. This helps us make better parts with fewer mistakes. The new method is faster and more accurate than the usual way of doing things, which takes a lot of time and computer power. Now we can use this AI to make predictions about different manufacturing processes without having to do all that extra work. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Temperature
