Mechanics & Industry
Volume 24, 2023
|Number of page(s)||16|
|Published online||21 March 2023|
Multi-objective optimization of process parameters for ultrasonic rolling extrusion of 42CrMo material
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
* e-mail: firstname.lastname@example.org
Accepted: 26 January 2023
To choose the most suitable method to solve the process parameter optimization of ultrasonic rolling extrusion, the 42CrMo material was taken as the research object. Based on a four-factor five-level orthogonal experiment, the response surface method was used to establish prediction models of the surface roughness, surface residual stress, and work hardening degree. To obtain better Pareto front, resulting in better distribution and convergence of the solution set, the simulated annealing algorithm, particle swarm optimization, second-generation non-dominated sorting genetic algorithm and multi-island genetic algorithm were used to optimize the parameters of ultrasonic rolling extrusion. Comparing the optimization effect with the calculation efficiency, the simulated annealing algorithm is finally selected as the optimization method of the ultrasonic rolling extrusion process parameters, and the optimization parameter domain of the ultrasonic rolling extrusion process is obtained. The optimization model was tested and verified. The results showed that the best optimization effect was achieved after 3000 iterations, and the maximum relative error of the experimental and calculated values for the surface roughness, work hardening degree and residual stress of the optimized 42CrMo material after ultrasonic rolling was controlled within 5%. The established multi-objective optimization model has high accuracy and application value, can realize the optimization of ultrasonic rolling extrusion process parameters.
Key words: 42CrMo / surface properties / ultrasonic roll extrusion / multi-objective optimization / SA / PSO / NSGA-II / MIGA
Note to the reader: The corresponding author was corrected on 27 March 2023.
© X. Wang et al., Published by EDP Sciences 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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