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Optimization of the ultrasonic roll extrusion process parameters based on the SPEA2SDE algorithm.

Xiaoqiang Wang1,2, Haojie Wang3,4, Paigang Wang1,2

  • 1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.

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Summary

Optimizing ultrasonic roll extrusion for 42CrMo bearing steel involves finding ideal processing parameters. A novel SPEA2SDE algorithm effectively predicts and achieves optimal surface roughness, residual stress, and hardness, guiding production.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Manufacturing Processes

Background:

  • Ultrasonic roll extrusion is a key manufacturing process for enhanced material properties.
  • Optimizing processing parameters is crucial for achieving desired surface characteristics in bearing steel.

Purpose of the Study:

  • To determine optimal processing parameters for ultrasonic roll extrusion of 42CrMo bearing steel.
  • To establish reliable prediction models for surface roughness, residual stress, and hardness.
  • To validate a new multi-objective optimization algorithm for manufacturing processes.

Main Methods:

  • Orthogonal experimental design was employed to study spindle speed, feed speed, static pressure, and amplitude.
  • Multiple regression analysis was used to build prediction models for surface properties.
  • The SPEA2SDE algorithm was developed and compared against NSGA II and SPEA2 for multi-objective optimization.

Main Results:

  • Prediction models for surface roughness, residual stress, and hardness were successfully established and verified.
  • The SPEA2SDE algorithm demonstrated high precision, with an average error within 10% compared to experimental values.
  • Optimized parameters significantly improved surface roughness, residual stress, and hardness.

Conclusions:

  • The SPEA2SDE algorithm is effective for multi-objective optimization of ultrasonic roll extrusion parameters.
  • The findings provide a reliable method for guiding actual production machining and improving component quality.
  • This research contributes to the advancement of precision manufacturing techniques for bearing steels.