Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers

Open Access

Year : 2025 | Volume : 13 | Special Issue 01 | Page : 1083 1096
    By

    Nishant Kumar Dhiman,

  • Sandeep M Salodkar,

  • Daljeet Singh,

  • Chanderkant Susheel,

  1. Research Scholar, Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh, Punjab, India
  2. Associate Professor, Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh, Punjab, India
  3. Research Scholar, Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Ropar, Punjab, India
  4. Assistant Professor, Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh, Punjab, India

Abstract

Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material science techniques with advanced data analytics. Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS) were used to confirm the uniform distribution of iron particles within the MRE samples, ensuring material integrity. Magnetorheological testing data were subsequently collected and utilized for training and validating machine learning algorithms, specifically Random Forest and Gradient Boosting models. These algorithms were rigorously evaluated using established metrics such as R-squared (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) to determine their predictive performance. The results demonstrated high accuracy, with R² values exceeding 90% for both training and validation datasets. Furthermore, exploratory analysis, including partial dependence plots and feature importance rankings, provided deeper insights into how factors such as Carbonyl Iron Percentage, curing magnetic field, and strain rate influence the complex shear modulus. This research bridges the gap between empirical material science and computational prediction methods, offering a robust framework for predicting the viscoelastic behavior of MREs. The findings contribute to the optimization of MREs for real-world applications, enhancing their potential in smart material systems.

Keywords: Soft magnetorheological elastomers (MREs), machine learning, regression models, rheological behaviour, ferromagnetic particles, curing magnetic field, complex shear modulus

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

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How to cite this article:
Nishant Kumar Dhiman, Sandeep M Salodkar, Daljeet Singh, Chanderkant Susheel. Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers. Journal of Polymer and Composites. 2024; 13(01):1083-1096.
How to cite this URL:
Nishant Kumar Dhiman, Sandeep M Salodkar, Daljeet Singh, Chanderkant Susheel. Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers. Journal of Polymer and Composites. 2024; 13(01):1083-1096. Available from: https://journals.stmjournals.com/jopc/article=2024/view=189614


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Special Issue Open Access Review Article
Volume 13
Special Issue 01
Received 20/03/2024
Accepted 16/10/2024
Published 16/12/2024
Publication Time 271 Days
360

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