The Use of Machine Learning for the Prediction of the Uniformity of the Degree of Cure of a Composite in an Autoclave

The difference in the degree of cure of the composite in an autoclave is one of the main characterization parameters of the uniformity of the degree of cure of the composite material. Therefore, it is very important to develop an effective method for predicting the difference in the curing degree of...

Full description

Saved in:
Bibliographic Details
Published inAerospace Vol. 8; no. 5; p. 130
Main Authors Lin, Yuan, Guan, Zhidong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2021
Subjects
Algorithms
Artificial neural networks
autoclave
Autoclaves
Composite materials
composites materials
Curing
curing process
estimation
Heat transfer
Heating rate
Machine learning
Molding (process)
Neural networks
Parameter estimation
Radial basis function
Regression models
residual stress
Simulation
Solidification
Support vector machines
Online AccessGet full text

Cover

More Information
Summary:The difference in the degree of cure of the composite in an autoclave is one of the main characterization parameters of the uniformity of the degree of cure of the composite material. Therefore, it is very important to develop an effective method for predicting the difference in the curing degree of a composite autoclave to improve the uniformity of the curing degree of the composite materials. We researched five machine learning models: a fully connected neural network (FCNN) model, a deep neural network (DNN) model, a radial basis function (RBF) neural network model, a support vector regression (SVR) model and a K-nearest neighbors (KNN) model. We regarded the heating rate, holding time and holding temperature of the composite material’s two holding-stage cure profile as input parameters and established a rapid estimation model of the maximum curing degree difference at any time during the molding process. We simulated the molding process of the composite material in an autoclave to obtain the maximum difference in the curing degree as the test sample data to train five machine learning models and compared and verified the different models after the training. The results showed that the RBF neural network model had the best prediction effect among the five models and the RBF was the most suitable algorithm for this model.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace8050130