Non-parametric multiple inputs prediction model for magnetic field dependent complex modulus of magnetorheological elastomer

• Main Authors: Saharuddin, Kasma Diana, Mohammed Ariff, Mohd. Hatta, Bahiuddin, Irfan, Ubaidillah, Ubaidillah, Mazlan, Saiful Amri, Abdul Aziz, Siti Aishah, Nazmi, Nurhazimah, Abdul Fatah, Abdul Yasser, Shapiai, Mohd. Ibrahim
• 格式: Article
• 语言: English
• 出版: Nature Research 2022
• 主题: TJ Mechanical engineering and machinery
• 在线阅读: http://eprints.utm.my/103975/1/MohdHattaMohammad2022_NonParametricMultipleInputsPrediction.pdf; http://eprints.utm.my/103975/; http://dx.doi.org/10.1038/s41598-022-06643-4

This study introduces a novel platform to predict complex modulus variables as a function of the applied magnetic field and other imperative variables using machine learning. The complex modulus prediction of magnetorheological (MR) elastomers is a challenging process, attributable to the material’s highly nonlinear nature. This problem becomes apparent when considering various possible fabrication parameters. Furthermore, traditional parametric modeling methods are limited when applied to solve larger-scale cases involving large databases. Consequently, the application of non-parametric modeling such as machine learning has gained increasing attraction in recent years. Therefore, this work proposes a data-driven approach for predicting multiple input-dependent complex moduli using feedforward neural networks. Besides excitation frequency and magnetic flux density as operating conditions, the inputs consider compositions and curing conditions represented by magnetic particle weight percentage and the curing magnetic field, respectively. Extreme learning machines and artificial neural networks were used to train the models. The simulation results obtained at various curing conditions and other inputs confirm that the predicted complex modulus has high accuracy with an R2 of about 0.997, as compared to the experimental results. Furthermore, the predicted complex modulus pattern and magnetorheological effect agree with the experimental data using both the learned and unlearned data.