Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917577015427072 |
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| author | Harik, Ramy Kalach, Fadi El Samaha, Jad Clark, Devon Sander, Drew Samaha, Philip Burns, Liam Yousif, Ibrahim Gadow, Victor Tarekegne, Theodros Saha, Nitol |
| author_facet | Harik, Ramy Kalach, Fadi El Samaha, Jad Clark, Devon Sander, Drew Samaha, Philip Burns, Liam Yousif, Ibrahim Gadow, Victor Tarekegne, Theodros Saha, Nitol |
| contents | Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence models applicable for the manufacturing industry. Finally, these datasets are the first iteration of published data from the future Factories lab and can be further adjusted to fit more researchers needs moving forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform Harik, Ramy Kalach, Fadi El Samaha, Jad Clark, Devon Sander, Drew Samaha, Philip Burns, Liam Yousif, Ibrahim Gadow, Victor Tarekegne, Theodros Saha, Nitol Machine Learning Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence models applicable for the manufacturing industry. Finally, these datasets are the first iteration of published data from the future Factories lab and can be further adjusted to fit more researchers needs moving forward. |
| title | Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.15544 |