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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Veröffentlicht in:	Journal of materials engineering and performance Jg. 33; H. 8; S. 3787 - 3800
Hauptverfasser:	Xue, Huan, Li, Tao, Li, Jie, Zhang, Yansong, Huang, Shiyao, Li, Yongchun, Yang, Chongwen, Zhang, Wenqian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2024 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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