Multi-Objective Optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and Improved Particle Swarm Optimization

Austenite stainless steel of type 304 is one of the most difficult materials to process. During the machining process, parts easily generate higher surface residual stress in cutting direction (CD) and production costs. To solve the problem, a multi-objective optimization method that combined Dung B...

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Vydáno v:Journal of materials engineering and performance Ročník 33; číslo 8; s. 3787 - 3800
Hlavní autoři: Xue, Huan, Li, Tao, Li, Jie, Zhang, Yansong, Huang, Shiyao, Li, Yongchun, Yang, Chongwen, Zhang, Wenqian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2024
Springer Nature B.V
Témata:
Artificial neural networks
Austenitic stainless steels
Back propagation networks
Beetles
Characterization and Evaluation of Materials
Chemistry and Materials Science
Chromosomes
Computer simulation
Corrosion and Coatings
Datasets
Diesel engines
Dung
Efficiency
Energy consumption
Engineering Design
Finite element method
Genetic algorithms
Material removal rate (machining)
Materials Science
Mathematical models
Multiple objective analysis
Mutation
Neural networks
Optimization algorithms
Original Research Article
Parameters
Pareto optimization
Particle swarm optimization
Production costs
Productivity
Quality Control
Reliability
Residual stress
Safety and Risk
Stainless steel
Tribology
Turning (machining)
improved particle swarm optimization algorithm
residual stress
dung beetle optimizer algorithm
turning process
multi-objective optimization
304 stainless steel
ISSN:1059-9495, 1544-1024
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Shrnutí:Austenite stainless steel of type 304 is one of the most difficult materials to process. During the machining process, parts easily generate higher surface residual stress in cutting direction (CD) and production costs. To solve the problem, a multi-objective optimization method that combined Dung Beetle Optimizer-Back Propagation Neural Network (DBO-BPNN) and Improved Particle Swarm Optimization (IPSO) algorithm was adopted. Firstly, BPNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and DBO-BPNN were established to map the nonlinear relationship between surface residual stress in CD and turning parameters. Secondly, a dataset of surface residual stress in CD and material removal rate under different turning parameters was obtained through finite element method (FEM) turning simulation. The dataset was applied into neural network to establish a nonlinear mapping relationship between turning parameters and surface residual stress in CD. The turning parameters are used as variables, the surface residual stress in CD and material removal rate are applied as objective functions. The optimal Pareto solution set of the surface residual stress in CD and material removal rate was acquired by combining DBO-BPNN and IPSO. Finally, turning experiments were designed to verify the accuracy of the turning simulation. The study shows that the surface residual stress in CD decreased by 38.47%, and the material removal rate increased by 91.69%.
Bibliografie:ObjectType-Article-1
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ISSN:1059-9495
1544-1024
DOI:10.1007/s11665-023-08871-9