Summary of Scalable Ai Framework For Defect Detection in Metal Additive Manufacturing, by Duy Nhat Phan et al.
Scalable AI Framework for Defect Detection in Metal Additive Manufacturing
by Duy Nhat Phan, Sushant Jha, James P. Mavo, Erin L. Lanigan, Linh Nguyen, Lokendra Poudel, Rahul Bhowmik
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 The paper introduces a novel approach to detecting defects in metal parts produced through Additive Manufacturing (AM). Convolutional Neural Networks (CNN) are used to analyze thermal images of printed layers, automatically identifying anomalies that impact mechanical properties. The authors also investigate synthetic data generation techniques to address the limited and imbalanced AM training data. The models’ defect detection capabilities were assessed using real-world data from a laser powder bed fusion AM machine and synthetic datasets with and without noise. The results show significant accuracy improvements with synthetic data, highlighting the importance of expanding training sets for reliable defect detection. Specifically, Generative Adversarial Networks (GAN)-generated datasets streamlined data preparation by eliminating human intervention while maintaining high performance. Additionally, the denoising approach effectively improves image quality, ensuring reliable defect detection. The work integrates these models into the CLoud ADditive MAnufacturing (CLADMA) module, a user-friendly interface, to enhance accessibility and practicality for AM applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computers to look at pictures of metal parts made with a new way of making things called Additive Manufacturing. These computers can find problems in the metal that make it weaker or more likely to break. The researchers also tried to create fake data to help the computers learn faster and better. They tested how well the computers worked using real pictures of metal parts and fake ones with and without extra noise added. The results show that making fake data helps a lot, especially when using special computer programs called Generative Adversarial Networks. This makes it easier for people to use these computers to find problems in metal parts. |
Keywords
» Artificial intelligence » Cnn » Gan » Synthetic data
