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IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing
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Author(s): Xiangqian Wang (East China Normal University, China & Pingdingshan University, China), Haifeng Hu (Pingdingshan University, China), Yuyao Wang (Lamar University, USA)and Zhaoyu Wang (Fujian Normal University, China)
Copyright: 2024
Volume: 36
Issue: 1
Pages: 25
Source title:
Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.336482
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Abstract
Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.
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