Summary of An Augmented Surprise-guided Sequential Learning Framework For Predicting the Melt Pool Geometry, by Ahmed Shoyeb Raihan et al.
An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry
by Ahmed Shoyeb Raihan, Hamed Khosravi, Tanveer Hossain Bhuiyan, Imtiaz Ahmed
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 novel surprise-guided sequential learning framework, SurpriseAF-BO, integrates Artificial Intelligence (AI) into Metal Additive Manufacturing (MAM), offering a significant shift in MAM. The framework models the dynamics between process parameters and melt pool characteristics with limited data, a key benefit in MAM’s cyber manufacturing context. Compared to traditional machine learning (ML) models, SurpriseAF-BO shows enhanced predictive accuracy for melt pool dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAM is a new way of making things by adding layers of metal together. It can make very detailed and complex shapes, but it’s hard to get the same quality every time. To solve this problem, researchers used artificial intelligence (AI) to learn how different settings affect the melted metal pool. They created a special program that can predict what will happen with less data than usual, making it useful for MAM. |
Keywords
* Artificial intelligence * Machine learning
