4.8 Article

Machine and Feedstock Interdependence Modeling for Manufacturing Networks Performance Analysis

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 8, Pages 5067-5076

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3129003

Keywords

Reliability; Degradation; Manufacturing systems; Computational modeling; Production; Performance analysis; Loss measurement; Interdependence; manufacturing network; performance analysis; quality; reliability

Funding

  1. National Natural Science Foundation of China [71871181, 71631001, 71771186]
  2. 111 Project [B13044]
  3. Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX202028]
  4. Harold and Inge Marcus Professorship

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The paper investigates the impact of low-quality feedstocks on the performance of manufacturing systems and proposes an effective method for computing the performance of networked manufacturing systems. The method takes into account the interdependence between machines and low-quality feedstocks and evaluates the operational performance using models and algorithms.
The input of low-quality feedstocks triggers the interdependence between workpiece quality and machine reliability, which will further adversely impact the performance of manufacturing systems. Considering the interconnected manufacturing system structures, our primary goal is to provide an effective method to compute the performance of networked manufacturing systems suffering from machine and low-quality feedstock interdependence. The strength of our work first lies in the model for the compound degradation process of machines and dissemination of low-quality feedstocks, which enables us to construct a response chain to model the interdependence between machines and feedstocks in the manufacturing network. Then, the second strength is the effective algorithm for the computation of route connectivity and quality loss of a manufacturing network based on the interdependence model. A computational experiment shows our models and algorithm can work well for evaluating the operational performance of manufacturing networks.

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