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Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm.

Nanqi Li1, ZuEn Shang1, Yang Zhao2

  • 1School of Mechanical Engineering, Liaoning Technical University, Fuxin, China.

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|January 17, 2025
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Optimizing titanium alloy milling parameters with a novel snake algorithm significantly improves surface quality. This method enhances wear resistance and fatigue strength, crucial for high-performance parts.

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Intelligence

Background:

  • Titanium alloys present machining challenges due to low thermal conductivity, low elastic modulus, and work hardening.
  • Surface quality is critical for wear resistance, fatigue strength, and corrosion resistance of manufactured parts.
  • Optimizing high-speed milling parameters is essential for improving titanium alloy component performance.

Purpose of the Study:

  • To develop and validate a multi-strategy fusion snake algorithm for optimizing titanium alloy milling parameters.
  • To enhance the surface quality, specifically surface roughness, of titanium alloy parts produced by high-speed milling.
  • To improve the prediction accuracy and convergence speed of optimization algorithms for machining processes.

Main Methods:

  • Establishment of a titanium alloy milling surface roughness prediction model using the response surface method.
  • Introduction of a multi-strategy fusion snake algorithm (MSSO) incorporating orthogonal matrix initialization, dynamic adaptive mechanisms, and a joint reverse strategy.
  • Comparative analysis of the MSSO algorithm against other optimization algorithms on five benchmark functions.
  • Experimental validation of the optimized milling parameters on Ti64 alloy.

Main Results:

  • The MSSO algorithm demonstrated superior convergence speed and accuracy compared to other benchmark algorithms.
  • The proposed optimization method successfully improved the surface roughness of Ti64 alloy by 55.7% compared to pre-optimization levels.
  • The multi-strategy fusion enhanced population diversity, accelerated convergence, and prevented local optima in the optimization process.

Conclusions:

  • The multi-strategy fusion snake algorithm is effective for optimizing milling parameters to enhance titanium alloy surface quality.
  • The developed method offers a robust approach to overcoming challenges in predicting and improving surface finish in high-speed milling.
  • Optimized milling parameters significantly contribute to improved service performance and component reliability of titanium alloy parts.