Summary of An Automated Machine Learning Approach to Inkjet Printed Component Analysis: a Step Toward Smart Additive Manufacturing, by Abhishek Sahu et al.
An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing
by Abhishek Sahu, Peter H. Aaen, Praveen Damacharla
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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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 proposes a machine learning-based architecture for microwave characterization of inkjet printed components on flexible substrates. The architecture uses multiple machine learning algorithms, automatically selecting the best one to extract material parameters from on-wafer measurements. The approach leverages the mutual dependence between material parameters and EM-simulated propagation constants to train machine learning models. These models are then used with measured propagation constants to extract ink conductivity and dielectric properties of test prototypes. The study compares four heuristic-based machine learning models, finding that eXtreme Gradient Boosted Trees Regressor (XGB) and Light Gradient Boosting (LGB) perform best for the characterization problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using machines to help us understand tiny components on flexible materials. These components are important because they can be used in lots of different things, like flexible phones or smart clothing. The researchers developed a new way to use machine learning to figure out what these components are made of and how they work. They tested four different methods and found that two of them worked really well. |
Keywords
* Artificial intelligence * Boosting * Machine learning
