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Risk Evaluation for a Manufacturing Process Based on a Directed Weighted Network.

Lixiang Wang1, Wei Dai1, Dongmei Sun1,2

  • 1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network-based approach to quantify manufacturing process risk by analyzing causal relationships between quality characteristics. The method uses information entropy to measure process risk, offering a new tool for quality control.

Keywords:
PMIMEdirected weighted networkinformation entropyrisk evaluation

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Area of Science:

  • Industrial Engineering
  • Systems Engineering
  • Data Science

Background:

  • Manufacturing process quality is often complex, involving intricate relationships between various quality characteristics.
  • Evaluating the overall risk of a manufacturing process requires understanding these interdependencies.

Purpose of the Study:

  • To develop a novel method for evaluating manufacturing process risk.
  • To represent quality characteristics and their causal effects using a directed weighted network.

Main Methods:

  • A multistage manufacturing process model was established to extract quality information.
  • Mixed embedded partial conditional mutual information (PMIME) was employed to analyze causal effects between quality characteristics.
  • Node centrality was measured using information entropy theory to assess local and indirect influences.

Main Results:

  • Quality characteristics were mapped as nodes in a directed weighted network, with causal relationships as edges and effect magnitudes as weights.
  • The entropy value of the network, derived from weighted node centrality, effectively represents manufacturing process risk.
  • The proposed method was validated using a public dataset.

Conclusions:

  • The directed weighted network approach, incorporating PMIME and information entropy, provides a robust framework for manufacturing process risk assessment.
  • This method offers a quantifiable measure of risk by analyzing the complex coupling of quality characteristics.
  • The findings contribute to enhanced quality control and risk management in manufacturing systems.