Hybrid Artificial Neural Network Long Short-Term Memory Framework for Predicting the Mechanical Behavior of Composite Materials

The process of predicting mechanical properties in composite materials is an important challenge owing to their nonlinear and composition-dependent nature. In this research, a hybrid deep learning architecture fusing Artificial Neural Network (ANN) with Long Short-Term Memory (LSTM) networks is employed for the prediction of tensile strength, flexural strength, impact strength, and hardness for different weight composition composites. The composite was prepared with fiber contents of 0%, 5%, 10%, and 15% and was tested mechanically with respect to four different tests—tensile test, flexural test, impact test, and hardness test—to study the influence of fiber content variation on the physical characteristics of the material. The experimental dataset was used for both training and validation, while the intermediate compositions were suitably estimated using the devised hybrid architecture. It is observed that the ANN LSTM model exhibits superior predictability with R2 greater than 0.996 in all cases, which validates its capability to model complex material-property relations. This hybridization is a very computationally efficient and reliable approach for material optimization by minimizing the need for large-scale experimental trials. The results substantiate the value of ANN LSTM hybridization as a very strong predictive tool for composite material engineering.