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A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm.

Hui Zheng1,2, Hanwen Guo3,4, Tonglin Pang3,4

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This study introduces an improved NSGA2-FCM algorithm for product module division, overcoming local optima in traditional clustering. The novel approach enhances clustering accuracy and optimizes module partitioning for better system design.

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Area of Science:

  • Engineering
  • Computer Science
  • Operations Research

Background:

  • Traditional clustering algorithms often yield suboptimal solutions due to local optima.
  • Effective product module division is crucial for system design and manufacturing efficiency.

Purpose of the Study:

  • To propose an improved NSGA2-FCM algorithm for enhanced product module clustering.
  • To address the limitation of local optima in conventional clustering methods.
  • To optimize module division schemes for complex product systems.

Main Methods:

  • Improved Non-dominated Sorting Genetic Algorithm II (NSGA2) initialisation strategy combined with Fuzzy C-Means (FCM) clustering.
  • Functional Block Structure (FBS) mapping for product system functional structure modeling.
  • Correlation synthesis matrix construction based on module division drivers.

Main Results:

  • The improved NSGA2-FCM algorithm effectively avoids local optima.
  • Enhanced clustering accuracy achieved through the integration of FCM.
  • Optimized module partitioning solutions derived for product systems.
  • Demonstrated effectiveness in module classification of light industrial equipment, using beer fermenters as a case study.

Conclusions:

  • The developed NSGA2-FCM algorithm offers a robust solution for product module division.
  • The method improves exploration of the solution space for optimal partitioning.
  • Validated effectiveness for real-world applications in industrial equipment design.