Open AccessAn assembly process parameters optimization method for precision assembly performance
Author(s) -
Chunfu Shao,
Xin Ye,
Lei Wang,
Zhijing Zhang,
Dongsheng Zhu,
Jiahui Qian
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1303/1/012136
Subject(s) - offset (computer science) , process (computing) , artificial neural network , computer science , genetic algorithm , software , engineering , artificial intelligence , machine learning , programming language , operating system
There are only few works in literature that suggest an assembly process optimization method based on manufacturing errors in the precision manufacturing area. A multi-objective assembly process parameters evaluation and optimization method for precision assembly performance of microstructures with manufacturing errors has been proposed in this paper. Based on the model with manufacturing errors, the ABAQUS software is used for simulation and calculation, and the assembly performance evaluation indexes of the microstructures under different assembly process parameters, such as stress value, stress distribution value and pose offset, are obtained. The mapping model of the key assembly process parameters and assembly performance is established based on BP neural network. Finally, the best assembly process parameters for the optimal assembly performance are solved based on the genetic algorithm, and the method has been verified by the optimization results of preload forces of the 3D mechanism, which can be used to guide and monitor the assembly process quantitatively in the precision manufacturing area.