Industrial Internet of Things on integrated preventive maintenance and enterprise-resource-planning systems: A case study of fastener forming manufacturing processes. (May 2023)

• Record Type: Journal Article
• Authors: Hsu, Chih-Wei; Lu, Jui-Han; Wang, To-Cheng; Huang, Jui-Chan; Shu, Ming-Hung
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 237:Number 6/7(2023)
• Page Start: 1100
• Page End: 1114
• Publication Date: 2023-05
• Subjects: Industrial Internet of Things -- fastener forming process -- classification algorithms -- preventive maintenance -- enterprise resource planning -- manufacturing execution system; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/09544054221116703
• Languages: English
• ISSNs: 0954-4054

Abstract

The Industrial Internet of Things (IIoT) has evolved industrial operations to be more efficient and reliable. The fastener forming process (FFP) used to rely on the judgments of in-process inspectors and experienced operators. Since no critical information was transmitted between machines, the machining status during production was unable to reveal. Now, with sensors installed in the machines of FFP, manufacturing data can be collected, analyzed, and responded to the machines. This IIoT-embedded FFP can not only carry out real-time data, pre-processing, and feature engineering but also enable optimally selecting classification algorithms for preventive maintenance. Consequently, the FFP establishes machine-health indicators for the production site and the heading process recognizes the abnormal situations between punches. The operator-delayed response leads to a great amount of loss that can be avoided. Besides, with the integration of enterprise resource planning (ERP) and manufacturing execution system (MES), this IIoT-embedded FFP converts receiving customer orders into work orders and coordinates operations and sales within enterprises. This system integration catches the status of various production indicators and further advances accurate materials preparation and inventory costs control. Overall, this IIoT-embedded FFP system becomes no necessity for increasing inventory and reserving backlog funds to cope with the impact of a vast number of scrap products. Finally, other types of machining processes or systems can adopt this systematic approach in the future.