STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Review Article Special issue Open Access

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

  1. Nishant Kumar Dhiman Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh
  2. Sandeep M Salodkar Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh
  3. Daljeet Singh Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Ropar
  4. Chanderkant Susheel Department of Mechanical Engineering, Punjab Engineering College Deemed to be University, Chandigarh

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

References (42)

  1. Ginder JM, Schlotter WF, Nichols ME. Magnetorheological elastomers in tunable vibration absorbers. SPIE Proceedings. 2001;4331:103-110. doi:10.1117/12.432694
  2. GINDER JM, CLARK SM, SCHLOTTER WF, NICHOLS ME. MAGNETOSTRICTIVE PHENOMENA IN MAGNETORHEOLOGICAL ELASTOMERS. International Journal of Modern Physics B. 2002;16(17n18):2412-2418. doi:10.1142/s021797920201244x
  3. Li Y, Li J, Tian T, Li W. A highly adjustable magnetorheological elastomer base isolator for applications of real-time adaptive control. Smart Materials and Structures. 2013;22(9):095020. doi:10.1088/0964-1726/22/9/095020
  4. Li Y, Li J, Li W, Samali B. Development and characterization of a magnetorheological elastomer based adaptive seismic isolator. Smart Materials and Structures. 2013;22(3):035005. doi:10.1088/0964-1726/22/3/035005
  5. Sun S, Peng X, Guo Z. Study on Macroscopic and Microscopic Mechanical Behavior of Magnetorheological Elastomers by Representative Volume Element Approach. Advances in Condensed Matter Physics. 2014;2014:1-8. doi:10.1155/2014/232510
  6. Susheelkumar GN, Murigendrappa SM, Gangadharan KV. Theoretical and experimental investigation of model-free adaptive fuzzy sliding mode control for MRE based adaptive tuned vibration absorber. Smart Materials and Structures. 2019;28(4):045017. doi:10.1088/1361-665x/ab04b6
  7. Oh JS, Choi SB. Medical applications of magnetorheological fluids—a review. Magnetic Materials and Technologies for Medical Applications. 2022:485-500. doi:10.1016/b978-0-12-822532-5.00008-x
  8. Ahamed R, Choi SB, Ferdaus MM. A state of art on magneto-rheological materials and their potential applications. Journal of Intelligent Material Systems and Structures. 2018;29(10):2051-2095. doi:10.1177/1045389x18754350
  9. Kang S, Choi K, Nam JD, Choi H. Magnetorheological Elastomers: Fabrication, Characteristics, and Applications. Materials. 2020;13(20):4597. doi:10.3390/ma13204597
  10. Zhou GY. Complex shear modulus of a magnetorheological elastomer. Smart Materials and Structures. 2004;13(5):1203-1210. doi:10.1088/0964-1726/13/5/024
  11. Behrooz M, Wang X, Gordaninejad F. Performance of a new magnetorheological elastomer isolation system. Smart Materials and Structures. 2014;23(4):045014. doi:10.1088/0964-1726/23/4/045014
  12. Bastola AK, Hossain M. The shape – morphing performance of magnetoactive soft materials. Materials & Design. 2021;211:110172. doi:10.1016/j.matdes.2021.110172
  13. Han Y, Zhang Z, Faidley LE, Hong W. Microstructure-based modeling of magneto-rheological elastomers. SPIE Proceedings. 2012;8342:83421B. doi:10.1117/12.925492
  14. Jolly MR, Carlson JD, Muñoz BC. A model of the behaviour of magnetorheological materials. Smart Materials and Structures. 1996;5(5):607-614. doi:10.1088/0964-1726/5/5/009
  15. Ivaneyko D, Toshchevikov V, Saphiannikova M, Heinrich G. Mechanical properties of magneto-sensitive elastomers: unification of the continuum-mechanics and microscopic theoretical approaches. Soft Matter. 2013;10(13):2213-2225. doi:10.1039/c3sm52440j
  16. Mukherjee D, Bodelot L, Danas K. Microstructurally-guided explicit continuum models for isotropic magnetorheological elastomers with iron particles. International Journal of Non-Linear Mechanics. 2020;120:103380. doi:10.1016/j.ijnonlinmec.2019.103380
  17. Nedjar B. An anisotropic viscoelastic fibre–matrix model at finite strains: Continuum formulation and computational aspects. Computer Methods in Applied Mechanics and Engineering. 2007;196(9-12):1745-1756. doi:10.1016/j.cma.2006.09.009
  18. Alexander RL. Viscoelastic Models in the Physics Curriculum. The Physics Teacher. 1965;3(8):350-353. doi:10.1119/1.2349207
  19. Bulíček M, Málek J, Rajagopal KR. On Kelvin-Voigt model and its generalizations. Evolution Equations and Control Theory. 2012;1(1):17-42. doi:10.3934/eect.2012.1.17
  20. Agirre-Olabide I, Lion A, Elejabarrieta MJ. A new three-dimensional magneto-viscoelastic model for isotropic magnetorheological elastomers. Smart Materials and Structures. 2017;26(3):035021. doi:10.1088/1361-665x/26/3/035021
  21. Yang S, Wang P, Liu Y, Dong X, Tong Y, Zhao Y. Modified Bouc-Wen Model Based on Fractional Derivative and Application in Magnetorheological Elastomer. Frontiers in Materials. 2021;8. doi:10.3389/fmats.2021.743716
  22. Dargahi A, Rakheja S, Sedaghati R. Development of a field dependent Prandtl-Ishlinskii model for magnetorheological elastomers. Materials & Design. 2019;166:107608. doi:10.1016/j.matdes.2019.107608
  23. Nadzharyan TA, Kostrov SA, Stepanov GV, Kramarenko EY. Fractional rheological models of dynamic mechanical behavior of magnetoactive elastomers in magnetic fields. Polymer. 2018;142:316-329. doi:10.1016/j.polymer.2018.03.039
  24. Yu Y, Li Y, Li J, Gu X. A hysteresis model for dynamic behaviour of magnetorheological elastomer base isolator. Smart Materials and Structures. 2016;25(5):055029. doi:10.1088/0964-1726/25/5/055029
  25. Saharuddin KD, Ariff MHM, Bahiuddin I, Ubaidillah U, Mazlan SA, Aziz SAA, et al. Non-parametric multiple inputs prediction model for magnetic field dependent complex modulus of magnetorheological elastomer. Scientific Reports. 2022;12(1). doi:10.1038/s41598-022-06643-4
  26. Saharuddin KD, Mohammed Ariff MH, Bahiuddin I, Mazlan SA, Abdul Aziz SA, Nazmi N, et al. Constitutive models for predicting field-dependent viscoelastic behavior of magnetorheological elastomer using machine learning. Smart Materials and Structures. 2020;29(8):087001. doi:10.1088/1361-665x/ab972d
  27. Hao H, Zhang C. Dynamic modeling of magnetorheological elastomer base isolator based on extreme learning machine. Mechanics of Structures and Materials XXIV. 2019:732-737. doi:10.1201/9781315226460-119
  28. Bahiuddin I, Wahab NA, Shapiai MI, Mazlan SA, Mohamad N, Imaduddin F, et al. Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method. Journal of Intelligent Material Systems and Structures. 2019;30(11):1727-1742. doi:10.1177/1045389x19844007
  29. Yu Y, Li Y, Li J, Gu X. Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator. Neurocomputing. 2016;211:41-52. doi:10.1016/j.neucom.2016.02.074
  30. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2014;13:8-17. doi:10.1016/j.csbj.2014.11.005
  31. Ahmed S, Alshater MM, Ammari AE, Hammami H. Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance. 2022;61:101646. doi:10.1016/j.ribaf.2022.101646
  32. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artif Intell Rev, vol. 54, no. 3, pp. 1937–1967, Mar. 2021, doi:10.1007/S10462-020-09896-5/METRICS.
  33. Yu Y, Li Y, Li J. Parameter identification of a novel strain stiffening model for magnetorheological elastomer base isolator utilizing enhanced particle swarm optimization. Journal of Intelligent Material Systems and Structures. 2014;26(18):2446-2462. doi:10.1177/1045389x14556166
  34. Yu Y, Li Y, Li J. Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm. Journal of Intelligent Material Systems and Structures. 2015;26(14):1789-1798. doi:10.1177/1045389x15577649
  35. Leng D, Xu K, Ma Y, Liu G, Sun L. Modeling the behaviors of magnetorheological elastomer isolator in shear-compression mixed mode utilizing artificial neural network optimized by fuzzy algorithm (ANNOFA). Smart Materials and Structures. 2018;27(11):115026. doi:10.1088/1361-665x/aadfa9
  36. Wu C, Cheng C, El-Aty AA, Li T, Qin Y, Yang Q, et al. Influence of particles size and concentration of carbonyl iron powder on magnetorheological properties of silicone rubber-based magnetorheological elastomer. Materials Research Express. 2020;7(8):086101. doi:10.1088/2053-1591/abaf8a
  37. Chen L, Gong XL, Li WH. Microstructures and viscoelastic properties of anisotropic magnetorheological elastomers. Smart Materials and Structures. 2007;16(6):2645-2650. doi:10.1088/0964-1726/16/6/069
  38. Wang ZL, Adachi Y. Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach. Materials Science and Engineering: A. 2019;744:661-670. doi:10.1016/j.msea.2018.12.049
  39. Ding, L. Y. Liu, T. L. Liu, Y. Q. Li, J. P. Li, and Z. Y. Sun, “Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning,” Chinese Journal of Polymer Science (English Edition), vol. 41, no. 3, pp. 422–431, Mar. 2023, doi:10.1007/S10118-022-2838-6/METRICS.
  40. Hasan M, Acar P. Machine learning reinforced microstructure-sensitive prediction of material property closures. Computational Materials Science. 2022;210:110930. doi:10.1016/j.commatsci.2021.110930
  41. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front Neurorobot, vol. 7, no. DEC, p. 63623, Dec. 2013, doi:10.3389/FNBOT.2013.00021/BIBTEX.
  42. Liu, Y. Wang, and J. Zhang, “New machine learning algorithm: Random forest,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7473 LNCS, pp. 246–252, 2012, doi:10.1007/978-3-642-34062-8_32/COVER.
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