Predicting mechanical properties of magnetorheological elastomers during the manufacturing process using a new machine learning method

Abstract

Precisely predicting the shear storage modulus of magnetorheological elastomer (MRE) is crucial for effective vibration control. Traditional methods, however, are time-consuming and resource-intensive. This study introduces a novel hybrid deep learning model (RICBM) to efficiently predict this modulus. RICBM combines Random Forest (RF), an improved football team training algorithm, convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and multi-head attention (MAT) mechanisms. Initially, the significance of chosen input features is assessed through the RF. Subsequently, spatial pyramid matching (SPM) chaotic mapping and nonlinear weighting factors are employed to enhance the performance of the Football team training algorithm (FTTA). Next, a CNN-BiLSTM model is developed and the improved FTTA (IFTTA) is utilized to refine its parameters. Ultimately, the multi-head attention mechanism is utilized to highlight crucial input features, thereby further enhancing the predictive capabilities of the model. MRE samples with varied preparation parameters are prepared and tested by a rheometer in this study, resulting in a database of MRE shear storage modulus with 6 input and 1 output feature. The proposed model is applied to this database, alongside various comparative models. The experimental results demonstrate that RF effectively processes data and enhances prediction accuracy. The IFTTA enhances the CNN-BiLSTM model's predictive accuracy for the shear storage modulus of MRE. The resulting model shows significant effectiveness in making these predictions.