Optimization of Injection Process Parameters for Composite Parts by BP Neural Network and Genetic Algorithm

WANG Bo; ZHAO Dongping; LI Feng; ZHAO Shimin; WANG Yizhuo

doi:10.11973/jxgccl202107012

Materials and Mechanical Engineering > 2021 > 45(7): 63-68. > DOI: 10.11973/jxgccl202107012

WANG Bo, ZHAO Dongping, LI Feng, ZHAO Shimin, WANG Yizhuo. Optimization of Injection Process Parameters for Composite Parts by BP Neural Network and Genetic Algorithm[J]. Materials and Mechanical Engineering, 2021, 45(7): 63-68. DOI: 10.11973/jxgccl202107012

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Optimization of Injection Process Parameters for Composite Parts by BP Neural Network and Genetic Algorithm

School of Aerocraft, Xi'an Aeronautical University, Xi'an 710077, China

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Received Date: July 02, 2020
Revised Date: May 16, 2021

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Abstract

Taking warpage deformation of the aircraft nose radome under different process parameters obtained by Moldflow software as training samples, a back propagation (BP) neural network model was established between the process parameters of the radome model and its warpage deformation values. Then genetic algorithm was used to optimize the process parameters, and the process parameters of the radome model with the smallest warpage deformation value was obtained. The results show that with the same process parameters, the warpage deformation value of the radome model obtained by BP neural network was similar to that simulated by Moldflow software, and the relative error was less than 4%, which proved the reliability of BP neural network. The simulated optimal molding process parameters of the radome model were injection temperature of 295 ℃, mold temperature of 80 ℃, injection time of 0.75 s, pressure holding time of 8 s, and pressure holding pressure of 125 MPa; the warpage deformation value was the smallest of 0.121 3 mm. The maximum warpage deformation of the radome model was 0.126 0 mm after injection molding with the optimal molding process parameters, and the ralative error between the experimental result and the predicted result was less than 3.7%, which verified the accuracy of the method of combining BP neural network and genetic algorithm.
- composite part,
- warpage deformation,
- BP neural network,
- genetic algorithm

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