Summary of Ads: Active Data-sharing For Data Quality Assurance in Advanced Manufacturing Systems, by Yue Zhao et al.
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
by Yue Zhao, Yuxuan Li, Chenang Liu, Yinan Wang
First submitted to arxiv on: 31 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 The proposed Active Data-sharing (ADs) framework aims to address the issue of data scarcity in industrial applications by enabling data-sharing among multiple machines with similar functionality. The framework simultaneously selects the most informative data points for building machine learning methods and mitigates distribution mismatch among selected data points. Evaluations on anomaly detection tasks using in-situ monitoring data from three additive manufacturing processes demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ADs framework helps to collect and share relevant data among machines, making it easier to train machine learning models for industrial applications. This improves the performance of these models, allowing them to make better predictions and decisions. The framework is designed specifically for use in additive manufacturing processes and could be applied to other similar industries. |
Keywords
* Artificial intelligence * Anomaly detection * Machine learning
