Summary of Advanced Predictive Quality Assessment For Ultrasonic Additive Manufacturing with Deep Learning Model, by Lokendra Poudel et al.
Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model
by Lokendra Poudel, Sushant Jha, Ryan Meeker, Duy-Nhat Phan, Rahul Bhowmik
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents a deep learning-based solution to monitor the quality of Ultrasonic Additive Manufacturing (UAM) processes. Specifically, it develops convolutional neural networks (CNNs) that can classify thermal images of UAM processing conditions with high accuracy. The CNN models are trained on five power levels and two scenarios: with or without thermocouples. The results show an overall accuracy of 98.29%, highlighting the system’s effectiveness in identifying defects and ensuring quality control in manufacturing environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a way to use special computers (deep learning-based) to look at pictures of metal-making processes. They wanted to see if these computers could tell when something was going wrong during this process. The computers were trained on lots of pictures taken from different angles and with different settings. Then, they tested how well the computers did by showing them more new pictures. It turns out that these computers are really good at figuring out what’s happening in those metal-making processes! They can help make sure that the final products are just right. |
Keywords
» Artificial intelligence » Cnn » Deep learning
