Advances in Smart Materials for Next-Generation Mechanical Systems

Authors

  • Berliana Setyaningrum Sekolah Tinggi Teknologi Angkatan Laut
  • Ahmad Lutfi Abdillah Sekolah Tinggi Teknologi Angkatan Laut
  • Mila Makhfiroh Sufrotul Laili Sekolah Tinggi Teknologi Angkatan Laut

Keywords:

Adaptive design, Mechanical systems, Piezoelectric materials, Shape memory alloys, Smart materials

Abstract

The rapid advancement of smart materials has significantly influenced the development of next-generation mechanical systems by enabling adaptive, efficient, and multifunctional designs. Smart materials, including shape memory alloys, piezoelectric materials, magnetorheological fluids, and self-healing polymers, exhibit unique properties that respond dynamically to external stimuli such as temperature, stress, electric fields, or magnetic fields. These materials are increasingly integrated into mechanical engineering applications ranging from aerospace and automotive systems to robotics and biomedical devices. Their ability to provide real-time adaptability enhances system performance, reduces energy consumption, and extends operational lifespan. Recent research has demonstrated the potential of shape memory alloys in actuators, the application of piezoelectric materials for energy harvesting, and the use of magnetorheological fluids in vibration control. Furthermore, self-healing polymers contribute to sustainability by improving material durability and reducing maintenance needs. Despite these advancements, challenges remain in terms of scalability, cost-effectiveness, and long-term stability, which limit widespread industrial adoption. Ongoing studies are addressing these limitations through the development of hybrid smart materials, advanced manufacturing processes, and computational modeling for predictive performance. This article provides a comprehensive overview of the recent progress in smart material technologies and their applications in next-generation mechanical systems. It highlights key innovations, identifies existing challenges, and outlines future directions for integrating smart materials into sustainable and high-performance engineering solutions.

References

Anton, S. R., & Sodano, H. A. (2007). A review of power harvesting using piezoelectric materials (2003–2006). Smart Materials and Structures, 16(3), R1–R21. https://doi.org/10.1088/0964-1726/16/3/R01

Auricchio, F., & Taylor, R. L. (1997). Shape-memory alloys: Modelling and numerical simulations of the finite-strain superelastic behavior. Computer Methods in Applied Mechanics and Engineering, 143(1–2), 175–194. https://doi.org/10.1016/S0045-7825(96)01147-7

Bhattacharya, K., & James, R. D. (2005). The material is the machine. Science, 307(5706), 53–54. https://doi.org/10.1126/science.1105169

Carlson, J. D., & Jolly, M. R. (2000). MR fluid, foam and elastomer devices. Mechatronics, 10(4–5), 555–569. https://doi.org/10.1016/S0957-4158(99)00064-1

Dagdeviren, C., Li, Z., & Wang, Z. L. (2017). Energy harvesting from the animal/human body for self-powered electronics. Annual Review of Biomedical Engineering, 19, 85–108. https://doi.org/10.1146/annurev-bioeng-071516-044517

Dagdeviren, C., et al. (2014). Conformal piezoelectric energy harvesting and storage from motions of the heart, lung, and diaphragm. Proceedings of the National Academy of Sciences, 111(5), 1927–1932. https://doi.org/10.1073/pnas.1317233111

Dyke, S. J., Spencer Jr., B. F., Sain, M. K., & Carlson, J. D. (1996). Modeling and control of magnetorheological dampers for seismic response reduction. Smart Materials and Structures, 5(5), 565–575. https://doi.org/10.1088/0964-1726/5/5/006

Hager, M. D., Greil, P., Leyens, C., van der Zwaag, S., & Schubert, U. S. (2010). Self-healing materials. Advanced Materials, 22(47), 5424–5430. https://doi.org/10.1002/adma.201003036

Janocha, H. (2007). Adaptronics and smart structures: Basics, materials, design, and applications. Springer. https://doi.org/10.1007/978-3-540-71965-2

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE.

Lagoudas, D. C. (Ed.). (2008). Shape memory alloys: Modeling and engineering applications. Springer. https://doi.org/10.1007/978-0-387-47685-8

Li, H., Cao, J., Qin, Q., & Han, Q. (2020). Recent advances in smart materials for intelligent manufacturing. Journal of Intelligent Material Systems and Structures, 31(17), 2050–2065. https://doi.org/10.1177/1045389X20924488

Liu, Y., Zhang, Y., & Leng, J. (2019). Advances in shape memory polymers and composites. Smart Materials and Structures, 28(10), 103003. https://doi.org/10.1088/1361-665X/ab3b6b

Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). SAGE Publications.

Mirzaeifar, R., & Gall, K. (2013). Prediction of fatigue behavior in shape memory alloys. International Journal of Plasticity, 44, 94–114. https://doi.org/10.1016/j.ijplas.2012.11.003

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Published

2025-01-30