Multi-Objective Optimization of Ultrasonic Surface Rolling Process Parameters for TC4 Titanium Alloy with IWOA-RBF...

Area of Science:

• Materials Science and Engineering
• Mechanical Engineering
• Computational Intelligence

Background:

• The ultrasonic surface rolling process (USRP) is crucial for enhancing TC4 titanium alloy surface properties.
• Standard optimization algorithms face challenges like premature convergence in complex parameter spaces.

Purpose of the Study:

• To develop a robust optimization framework for USRP parameters in TC4 titanium alloy.
• To improve surface characteristics including roughness, hardness, and residual compressive stress.

Main Methods:

• An improved Whale Optimization Algorithm (IWOA) was developed with enhanced initialization, convergence, and update strategies.
• An IWOA-optimized Radial Basis Function (RBF) neural network (IWOA-RBF) was created for predictive modeling.
• A multi-criteria decision-making framework (CRITIC-TOPSIS) integrated with multi-objective Grey Wolf Optimization (MOGWO) was employed for Pareto-based optimization.

Main Results:

• The IWOA-RBF model demonstrated high prediction accuracy for surface performance.
• Optimized USRP parameters led to significant reductions in surface roughness.
• Substantial improvements in surface hardness and residual compressive stress were achieved.

Conclusions:

• The proposed IWOA-RBF and MOGWO optimization framework is robust and effective.
• This strategy offers a reliable method for high-precision parameter optimization in USRP.
• Coordinated enhancement of surface properties in TC4 titanium alloy was successfully demonstrated.