Summary of Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform, by Ramy Harik et al.
Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform
by Ramy Harik, Fadi El Kalach, Jad Samaha, Devon Clark, Drew Sander, Philip Samaha, Liam Burns, Ibrahim Yousif, Victor Gadow, Theodros Tarekegne, Nitol Saha
First submitted to arxiv on: 28 Jan 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 two industry-grade datasets collected at the University of South Carolina’s Future Factories Lab. The datasets were generated by a manufacturing assembly line that followed industrial standards, with defects introduced to simulate real-world scenarios. One dataset is time-series analog, while the other is multi-modal, including images and analog data. These datasets aim to provide tools for enhancing intelligence in manufacturing, addressing the scarcity of real-world datasets with anomalies or defects. The datasets can be used to train Artificial Intelligence models applicable to the manufacturing industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper offers two unique datasets collected from a manufacturing assembly line that follows industrial standards. The datasets are designed to help researchers develop AI models for the manufacturing industry. One dataset is analog and time-series, while the other combines images and analog data. These datasets can be used to train AI models that learn to identify defects in real-world scenarios. |
Keywords
* Artificial intelligence * Multi modal * Time series
