Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 1 of its 4 references that are in the map. First posted on arXiv 28 Jan 2024.
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Citing papers per year: 2024: 3; 2025: 3; 2026: 1.
7 papers.
- 2024Time Series Foundational Models: Their Role in Anomaly Detection and Predictioncited by 4
- 2024AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelinescited by 3
- 2025NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelinescited by 3
- 2024Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturingcited by 2
- 2025CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturingcited by 2
- 2025SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturingcited by 0
- 2026Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Modelcited by 0
- 2026Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Model4 Jun 2026
- 2025CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing14 Oct 2025
- 2025SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing10 May 2025
- 2025NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines9 May 2025
- 2024Time Series Foundational Models: Their Role in Anomaly Detection and Prediction26 Dec 2024
- 2024AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines5 Aug 2024
- 2024Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing1 Jul 2024