Curing deformation prediction of L-shaped composite stringer based on machine learning

doi:10.19936/j.cnki.2096-8000.20241228.017

Abstract

Abstract: Aiming at the problem that the curing deformation mechanism of composite structure in the process of manufacturing is very complicated, and many parameters are involved and constantly change during the curing process, a method based on machine learning was proposed to predict the curing deformation of L-shaped composite stringer in the molding process. ABAQUS finite element software was used to simulate the curing molding process of L-shaped composite stringer in autoclave, and a data set of curing spring-in angle of L-shaped composite stringer was established, which was characterized by six parameters of the curing process temperature curve: two-stage heating rate, two-stage holding temperature and two-stage holding time. Then RBF neural network was constructed and curing deformation prediction was carried out. The results show that this method has high prediction accuracy and efficiency, the prediction error is less than 3%, and the model time is only 1.25 s.

Key words: composite, curing deformation, radical basis function, machine learning, L-shaped stringer

CLC Number:

TB332

SUN Xiaohui, LÜ Yi, WANG Jianjun, ZHANG Xianzhi, XIE Jiaqing. Curing deformation prediction of L-shaped composite stringer based on machine learning[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(12): 119-125.

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