An intelligent multi-objective optimization method for transverse profile grinding processes of large shafts

Large shaft grinding processes are required to generate multiple quality indices with good consistency, such as profile dimension accuracy, surface roughness, and optical glossiness. Generating profiles for large shafts in the grinding process requires the grinding wheels to move transversely under...

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Veröffentlicht in:International journal of advanced manufacturing technology Jg. 134; H. 7-8; S. 3787 - 3804
Hauptverfasser: Li, Xuekun, Ye, Meng, Tang, Zihan, Wang, Liping, Pan, Jiayu, Wang, Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.10.2024
Springer Nature B.V
Schlagworte:
Adaptive algorithms
CAE) and Design
Computer-Aided Engineering (CAD
Control methods
Design optimization
Engineering
Error compensation
Grinding wheels
Industrial and Production Engineering
Machining
Mechanical Engineering
Media Management
Multiple objective analysis
Optimization
Original Article
Particle swarm optimization
Process parameters
Profile grinding
Rough grinding
Surface properties
Surface roughness
Large shaft grinding
Multi-objective process optimization
Grinding quality and efficiency
ISSN:0268-3768, 1433-3015
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Zusammenfassung:Large shaft grinding processes are required to generate multiple quality indices with good consistency, such as profile dimension accuracy, surface roughness, and optical glossiness. Generating profiles for large shafts in the grinding process requires the grinding wheels to move transversely under the desired path, which includes rough grinding, semi-finish grinding, finish grinding, and spark-out grinding stages, and each grinding stage needs to go through multiple passes under different process parameters. Therefore, the design and selection of process parameters for each grinding stage is always challenging to meet both quality and efficiency requirements. In this study, a multi-objective optimization method for transverse profile grinding processes of large shafts is developed. In the proposed method, multiple grinding quality indices and grinding efficiency are taken as optimization objectives, and grinding surface quality, grinding depth, and grinder machining performance are taken as constraints. By utilizing the dynamic factor improvement strategy and the adaptive grid density improvement strategy, an improved particle swarm algorithm is developed to optimize the grinding process parameters. The grinding dimensional error compensation method and the surface quality consistency control method are integrated into the improved particle swarm algorithm to realize the accurate prediction of grinding dimensional accuracy and surface quality. Based on the proposed method, the optimized grinding process can be obtained, and the effectiveness of the method is verified by grinding experiments.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-14309-w