Summary of Advancing Additive Manufacturing Through Deep Learning: a Comprehensive Review Of Current Progress and Future Challenges, by Amirul Islam Saimon et al.
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
by Amirul Islam Saimon, Emmanuel Yangue, Xiaowei Yue, Zhenyu James Kong, Chenang Liu
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a comprehensive review of deep learning applications in additive manufacturing. It aims to bring together existing knowledge and encourage further development in this rapidly growing field. The authors cover three major areas: design for AM, AM modeling, and monitoring and control in AM. They use the PRISMA guidelines to select papers from Scopus and Web of Science databases that implement DL across seven major AM categories. The analysis reveals a trend towards using deep generative models for generative design in AM and incorporating process physics into DL models to improve AM process modeling and reduce data requirements. Additionally, there is growing interest in using 3D point cloud data for AM process monitoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep learning is used in making things layer by layer, a process called additive manufacturing. It brings together lots of research on this topic to help people understand what’s already been done and where the field is going. The authors looked at seven different ways of doing this kind of manufacturing and found that some techniques are better than others for certain types of designs. They also found that some deep learning models are good at making new designs, while others can help with monitoring how things are made. |
Keywords
* Artificial intelligence * Deep learning
