Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912223587205120 |
|---|---|
| author | Harik, Ramy Kalach, Fadi El Samaha, Jad Samaha, Philip Clark, Devon Sander, Drew Burns, Liam Yousif, Ibrahim Gadow, Victor Mahmoud, Ahmed Wuest, Thorsten |
| author_facet | Harik, Ramy Kalach, Fadi El Samaha, Jad Samaha, Philip Clark, Devon Sander, Drew Burns, Liam Yousif, Ibrahim Gadow, Victor Mahmoud, Ahmed Wuest, Thorsten |
| contents | This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2 Harik, Ramy Kalach, Fadi El Samaha, Jad Samaha, Philip Clark, Devon Sander, Drew Burns, Liam Yousif, Ibrahim Gadow, Victor Mahmoud, Ahmed Wuest, Thorsten Machine Learning This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects. |
| title | Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.05020 |