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Improved bat algorithm for roundness error evaluation problem.

Guowen Li1, Ying Xu1,2, Chengbin Chang1

  • 1School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

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|August 9, 2022
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Summary
This summary is machine-generated.

An improved bat algorithm (IBA) enhances roundness error assessment accuracy by incorporating logistic chaos initialization and sparrow search algorithm integration. This approach overcomes local extremes and premature convergence, improving precision in manufacturing.

Keywords:
bat algorithmconvergence speedroundness errorsolution accuracy

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Area of Science:

  • Manufacturing Engineering
  • Computational Intelligence
  • Metrology

Background:

  • Cutting tool wear, machine chatter, and poor lubrication degrade the roundness of precision shaft-hole parts, impacting product performance.
  • Accurate roundness error assessment is crucial for ensuring the quality and reliability of manufactured components.
  • Existing Bat Algorithm (BA) methods struggle with local extremes and premature convergence, limiting their effectiveness.

Purpose of the Study:

  • To develop an improved Bat Algorithm (IBA) for more accurate roundness error assessments in precision manufacturing.
  • To enhance the variation mechanisms and global search capabilities of the Bat Algorithm.
  • To validate the performance of the proposed IBA against existing algorithms and benchmark functions.

Main Methods:

  • The improved Bat Algorithm (IBA) incorporates logistic chaos initialization for better population variation and solution quality.
  • A sinusoidal control factor is introduced to dynamically adjust nonlinear inertia weights, balancing global and local search.
  • The Sparrow Search Algorithm (SSA) is integrated to improve the ability to escape local extremes and accelerate convergence.

Main Results:

  • IBA demonstrated superior performance over classical metaheuristic algorithms on eight benchmark functions, showing enhanced solution accuracy, convergence speed, and stability.
  • Simulations confirmed IBA's ability to efficiently locate the minimum inclusion region center with varying sampling points.
  • The roundness error values obtained using IBA were found to be more accurate than those from other tested algorithms.

Conclusions:

  • The proposed Improved Bat Algorithm (IBA) is a feasible and effective method for accurate roundness error evaluation in precision manufacturing.
  • IBA's enhancements significantly improve upon the original Bat Algorithm's limitations, offering better optimization and stability.
  • The integration of chaos theory and swarm intelligence provides a robust approach to addressing challenges in metrology and quality control.