Study on Prediction of Compression Performance of Composite Laminates After Impact Based on Convolutional Neural Networks

This paper proposed a method for predicting composite laminates' compressive residual strength after impact based on convolutional neural networks. Laminates made by M21E/IMA prepreg were used to introduce low-velocity impact damage and construct a non-destructive testing image dataset. The dataset images characterized the impact damage details, including dents, delamination, and matrix cracking. The convolution kernel automatically extracted and identified these complex features that could be used for classification. The model took the images as input and compressive residual strength labels as output for iterative training, and the final prediction accuracy reached more than 90%, the highest 96%. This method introduced overall damage into the model in the form of images utilizing convolution, which can quickly and accurately predicted laminates' compression performance after impact.

Automated summary

This paper presented a method for predicting the compressive residual strength of composite laminates after impact using convolutional neural networks. By constructing an image dataset to analyze impact damage details, the model achieved a prediction accuracy of over 90%.