Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life. (October 2020)

• Record Type: Journal Article
• Authors: Lenz, Juergen; MacDonald, Eric; Harik, Ramy; Wuest, Thorsten
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 274
• Page End: 286
• Publication Date: 2020-10
• Subjects: Smart product -- Smart manufacturing -- Integrated sensor system -- Data analytics -- PLM -- Industry 4.0; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.10.001
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Paradigm shift of leveraging Smart Products from the middle of life (MOL) to include the beginning of life (BOL) Assessment of feasibility for smart products use during BOL stage Sensor integration in Smart Products via additive manufacturing Prototypical demonstration of Smart Products in BOL via use case Abstract: The research objective of this work is to enhance the perception of, sensing in, and control of smart manufacturing systems (SMS) by leveraging active sensor systems within smart products during the manufacturing phase. Smart manufacturing utilizes rich process data, usually collected by the SMS (e.g., machine tools), to enable accurate tracking and monitoring of individual products throughout the process chain. However, until now, the to-be-manufactured product itself has not contributed to the sensing and compilation of product and process data. More specifically, data measured from the product's structure during its own fabrication. In this paper, we discuss and evaluate the opportunity to actively use the capabilities of smart products within a SMS in terms of technical and economic feasibility. This opportunity emerged only recently with the advancements in smart products engineering. In this research, we developed a smart product prototype and evaluated it on a SMS testbed (CPlab) with eight distinct, fully-connected manufacturing processes. The results of the conducted experiments show the possibility to uniquely identify two distinct ' fingerprints' of manufacturing processes solely based on data provided by sensors within the smart product itself. The sensor data was collected directly from the smart product before manufacture was completed, yet after the intended sensor functionality during the product's use phase was activated. The capability to automatically, accurately, and reliably identify process signatures and even inform the optimization of manufacturing parameters creates new opportunities for improvements in quality, scheduling, and seamless transparency across the whole value chain.