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Multi-Objective Process Parameter Optimization of Ultrasonic Rolling Combining Machine Learning and Non-Dominated

Junying Chen1, Tao Yang1, Shiqi Chen1

  • 1College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361000, China.

Materials (Basel, Switzerland)
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

Optimizing ultrasonic rolling parameters using machine learning and NSGA-II significantly improves surface integrity. This leads to enhanced hardness, reduced roughness, and a 52.5% increase in fatigue life for treated materials.

Keywords:
machine learningmulti-objective optimizationsurface integrityultrasonic rolling

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Manufacturing Processes

Background:

  • Surface integrity is crucial for material fatigue performance.
  • Ultrasonic rolling is a key technique for improving surface integrity.
  • Process parameters critically influence ultrasonic rolling outcomes.

Purpose of the Study:

  • To develop an optimized method for ultrasonic rolling process parameters.
  • To enhance surface integrity (residual stress, hardness, roughness) and fatigue life.
  • To combine machine learning (ML) with NSGA-II for multi-objective optimization.

Main Methods:

  • Trained five ML models to correlate process parameters with surface integrity metrics.
  • Incorporated feature augmentation and physical information into ML models.
  • Integrated the best ML model with NSGA-II for multi-objective optimization.
  • Conducted ultrasonic rolling tests and established a dataset.

Main Results:

  • Optimized parameters: 900 N static pressure, 75 rpm spindle speed, 0.19 mm/r feed rate, single pass.
  • Achieved -920.60 MPa surface residual stress and 958.23 HV surface hardness.
  • Reduced surface roughness from 0.54 µm to 0.32 µm.
  • Extended contact fatigue life to 3.02 × 10^7 cycles (52.5% improvement).

Conclusions:

  • The ML-NSGA-II approach effectively optimizes ultrasonic rolling parameters.
  • Optimized parameters significantly enhance surface integrity and fatigue performance.
  • This method offers a pathway to improved material durability and component lifespan.