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| Automatic Drilling and Riveting Process Parameter Optimization Method Based on BP Neural Network and Multi-Objective Particle Swarm Optimization Algorithm |
| LI Chao, WANG Zhongqi, CHANG Zhengping, MA Jianzhi |
| Northwestern Polytechnical University, Xi’an 710072, China |
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Abstract Interference connection is widely used in automatic drilling and riveting of aircraft panel. The coordinated control of the degree of uniformity of interference and panel deformation is an urgent problem to be solved. In this paper, an optimization method based on BP neural network (BPNN) is proposed. Taking the riveting force, riveting process time, riveting retention time and clamping force as variables, and taking the simulation data as samples, the prediction model of interference uniformity and wall deformation was established by using BP neural network. Multi-objective particle swarm optimization (MOPSO) algorithm was used for multi-objective optimization. The simulation and experimental results show that the optimized parameters can significantly improve the uniformity of interference and effectively reduce the deformation degree of plate.
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2021, 
