Manufacturing
The dataset were compiled and obtained using the HarmonyERP (https://www.harmonyerp.cloud/en/) software used by KNS Otomotiv (www.knsotomotiv.com/en/) which operates with the ATO model. The KNS company produces parts in various categories such as air duct systems, service sets, continuous LED lighting systems, grab bars, and baskets for commercial vehicles (buses).
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This represents a comprehensive collection of data from a Automative manufacturing unit. This unit could be involved in a range of production activities, from assembly line manufacturing to more complex, multi-stage processes. The dataset is designed to capture various operational parameters that are crucial for analyzing and optimizing manufacturing processes.
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Industrial cyber-physical systems (ICPS), which is the backbone of Industry 4.0, are the result of adapting emerging information communication technologies (ICT) to the industrial control systems (ICS). ICPS utilize autonomous robotic arms to accomplish manufacturing tasks. These arms follow a certain predetermined trajectory during the task.
In this dataset, we present four files generated from a setup that contains two Universal Robot UR3e collaborative robotic arms:
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Power transformers are inevitably subjected to external short circuit impact during their service period. The electromagnetic force generated by the fault current may cause winding destabilization and collapse. The radial buckling of the inner winding accounts for a considerable proportion. Based on the effective contact of the sticks, the traditional analytical methods ignore the manufacturing deviation and operation impact (MDOI) characterized by assembly gaps and insulation shrinkage.
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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.
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