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An Improved Multi-Objective Grey Wolf Optimizer for Bi-Objective Parameter Optimization in Single Point Incremental

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Summary

This study optimizes single point incremental forming (SPIF) for Al1060 sheets, reducing thinning and deviation. A novel framework using machine learning and advanced optimization significantly improves forming quality.

Keywords:
entropy-weighted TOPSISmulti-layer perceptronmulti-objective grey wolf optimizationprocess parameterssingle point incremental forming

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Mechanics

Background:

  • Single Point Incremental Forming (SPIF) is a flexible manufacturing process for sheet metals.
  • Excessive sheet thinning and geometric deviation are critical challenges in SPIF.
  • Optimizing SPIF process parameters is essential for achieving high-quality components.

Purpose of the Study:

  • To develop a bi-objective process parameter optimization framework for Al1060 sheets in SPIF.
  • To minimize sheet thinning and geometric deviation simultaneously.
  • To enhance the prediction accuracy and efficiency of forming quality.

Main Methods:

  • Utilized multilayer perceptron (MLP) as a surrogate model for rapid prediction of forming quality.
  • Employed an improved multi-objective grey wolf optimization (IMOGWO) algorithm for efficient Pareto optimal solution searching.
  • Incorporated Spm chaotic mapping, enhanced convergence coefficient updates, and associative learning within IMOGWO.
  • Applied entropy-weighted TOPSIS for selecting optimal parameters from the Pareto front.

Main Results:

  • The proposed framework effectively reduced sheet thinning and geometric deviation in SPIF.
  • MLP surrogate model enabled rapid and accurate prediction of forming quality.
  • IMOGWO algorithm efficiently identified Pareto optimal solutions for bi-objective optimization.
  • Experimental validation confirmed the simulation results with low relative errors (0.58% for thinning, 3.10% for deviation).

Conclusions:

  • The developed optimization framework is feasible and effective for improving SPIF quality.
  • The combination of MLP and IMOGWO offers a powerful approach for complex manufacturing process optimization.
  • The findings have practical potential for enhancing the precision and reliability of SPIF components.