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Improving operational reliability of manufacturing systems by process optimization via survival signatures

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Listed:
  • Liling Ge
  • Yingjie Zhang

Abstract

Considering that a mechanical part or component can be produced by varying process plans for specific manufacturing requirements, process optimization can be performed for some objectives, such as production quality, cost, and system reliability. The survival signature, as a reliability equivalence factor, is recently introduced to evaluate the performance reliability of a system. In this article, a survival signature–based process optimization approach is proposed to improve the operational reliability of the manufacturing process. First, a three-dimensional part model is analyzed for the identification of machining features, and the corresponding process strategies are made for creating them. Based on the feature process strategies and the current manufacturing resources, multi-process plans could be generated and a network is constructed, which illustrates the varying operation paths. Then the reliability distribution functions of the machines on the network are estimated by the failure data. Finally, the survival signatures of the network are computed and applied to measure the failure probabilities of all operation paths so that the optimal one could be selected. The feasibility and efficiency of the proposed approach have been demonstrated by a case study.

Suggested Citation

  • Liling Ge & Yingjie Zhang, 2019. "Improving operational reliability of manufacturing systems by process optimization via survival signatures," Journal of Risk and Reliability, , vol. 233(3), pages 444-454, June.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:3:p:444-454
    DOI: 10.1177/1748006X18799942
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    References listed on IDEAS

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