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Integrated mission reliability modeling for multistate manufacturing systems considering heterogeneous feedstocks based on extended stochastic flow manufacturing network

Author

Listed:
  • Yao, Jinyong
  • Gao, Zhanfei
  • He, Yihai
  • Peng, Chong

Abstract

Mission reliability is a crucial health indicator for systematically characterizing the operational state and degradation process of manufacturing systems. However, the impact of heterogeneous feedstocks (qualified feedstocks and unqualified feedstocks) on processing machine degradation and the reliability of buffers have not received much attention in the existing research on mission reliability modeling of manufacturing systems. Accordingly, this study proposes a novel integrated mission reliability modeling method for multistate manufacturing systems considering heterogeneous feedstocks. Specifically, based on the operational mechanism of a multistate manufacturing system considering heterogeneous feedstocks and the connotation of the integrated mission reliability of the manufacturing system, an extended stochastic flow manufacturing network (ESFMN) model is constructed to quantify the interaction among processing machines, inspection machines, buffers, and heterogeneous feedstocks. By analyzing the mixture degradation of the processing machine under the impact of heterogeneous feedstock, an integrated mission reliability model for a multistate manufacturing system considering heterogeneous feedstocks based on ESFMN is established. Finally, an industrial example of the valve spool manufacturing system is given to illustrate the effectiveness and superiority of the proposed method.

Suggested Citation

  • Yao, Jinyong & Gao, Zhanfei & He, Yihai & Peng, Chong, 2024. "Integrated mission reliability modeling for multistate manufacturing systems considering heterogeneous feedstocks based on extended stochastic flow manufacturing network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007548
    DOI: 10.1016/j.ress.2023.109840
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