Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives

Abstract

In this paper, we presented three neural network models including deep neural network (DNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM), which are proposed to predict the cohesive zone parameter of sintered silver DCB joint with different contents of nickel modified carbon nanotube, thus reducing the complexity of CZM parameter acquisition for nanoparticle reinforced adhesive. The bilinear CZM model is used as the prediction target model for sintered silver joints with different contents of nickel-modified carbon nanotube filler, and data sets suitable for different networks are established through experimental and numerical simulation results. Three kinds of networks are trained based on the optimized hyperparameters obtained from the Bayesian hyperparameters tuning process. The results show that DNN, RNN, and LSTM frameworks can all predict CZM parameters of nanoparticle-reinforced sintered silver adhesive through load-displacement curves. Based on loss analysis and statistical indicator comparison after K-fold cross-validation, the RNN and LSTM models have better prediction accuracy and performance than the DNN model, and the accuracy of the LSTM model is further improved compared with the DNN model. RNN and LSTM models have high prediction accuracy and stronger recognition ability for the time series data, they can be used as suitable alternative models for inverse recognition of CZM parameters of nanoparticle-reinforced adhesives, and have broad application prospects.