Khodaverdi Hesamodin, Sedaghati Ramin
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Polymers (Basel). 2025 Jul 9;17(14):1898. doi: 10.3390/polym17141898.
Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition-including matrix types, magnetic fillers, and additives-on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics.
磁流变（MR）泡沫材料是一类智能材料，在外部磁场作用下具有独特的可调粘弹性。这些材料将多孔结构与嵌入的磁性颗粒相结合，解决了传统磁流变液中常见的泄漏和沉降等问题，同时具有轻质设计、吸声、高能量收集能力和定制机械响应等优点。尽管它们具有潜力，但诸如颗粒分散不均匀、循环载荷下耐久性有限以及磁机械耦合不理想等挑战，仍然阻碍着它们的更广泛应用。本综述通过评估合成方法（非原位法与原位法）、微观结构设计策略以及不同固化条件下磁性颗粒排列的作用，系统地解决了这些问题。特别关注材料成分（包括基体类型、磁性填料和添加剂）对机械和磁流变行为的影响。虽然本综述的主要重点是MR泡沫材料，但也考虑了与MR弹性体相关的研究，因为它们具有共同的基本原理，以便提供更广泛的背景。还讨论了最近的进展，包括人工智能（AI）在预测MR材料的流变和磁机械行为、模拟复杂器件响应以及优化材料成分和加工条件方面的日益广泛应用。AI在MR系统中的应用范围从估计剪切应力、粘度以及储能/损耗模量，到分析非线性滞后、磁致伸缩和混合模式加载行为。这些数据驱动的方法为材料设计和性能优化提供了强大的新能力，有助于克服传统建模技术中长期存在的局限性。尽管MR泡沫材料取得了重大进展，但仍有几个挑战有待解决，包括实现颗粒均匀分散、提高粘弹性性能（储能模量和MR效应）以及改善循环载荷下的耐久性。解决这些问题对于在要求一致性能、机械可靠性和长期稳定性对安全、有效性和运行寿命至关重要的苛刻应用中释放MR泡沫材料的全部潜力至关重要。通过将实验方法、理论建模和AI驱动的设计相结合，这项工作确定了增强MR泡沫材料在振动阻尼、能量收集、生物医学设备和软机器人应用中的功能和可靠性的途径。
