Metamaterials and smart structures: Leveraging AI for design, optimization and adaptive engineering solutions

变形 独创性 定制 计算机科学 可扩展性 系统工程 可解释性 新兴技术 人工智能 灵活性(工程) 转化式学习 工程类 模块化(生物学) 航空航天 超材料 机器人学 标准化 重大挑战 智能材料 数据科学 适应(眼睛) 模块化程序设计 掩蔽
作者
Ogunniran, Abass Olalekan,OLUSESAN OLUWAFEMI ODUNAYO,Salako, Joseph Tosin,Edet, Anietie Ime,Oyelami, Elizabeth Olawumi,Akadiri, Oluwatoyin Olawale,Jimoh, Azeez Arisekola
出处
期刊:CERN European Organization for Nuclear Research - Zenodo [European Organization for Nuclear Research]
标识
DOI:10.5281/zenodo.17787577
摘要

In an era where engineering demands increasingly adaptive and resilient systems, this review delves into the profound synergy between metamaterials artificially engineered composites exhibiting extraordinary properties like negative refraction, tunable stiffness, and wave cloaking and smart structures, which embed sensing, actuation, and control mechanisms to dynamically respond to environmental stimuli, all amplified by the revolutionary power of artificial intelligence (AI). Exploring the fundamentals of metamaterials across electromagnetic, acoustic, mechanical, and thermal domains, alongside the principles of smart structures that enable self-monitoring and reconfiguration, the paper illuminates how AI techniques such as machine learning, deep learning, generative algorithms, and reinforcement learning transform design processes through inverse engineering, data-driven discovery, and multi-objective optimization, drastically reducing computational burdens and accelerating the creation of bespoke architectures for applications in aerospace morphing wings, seismic-resistant infrastructure, biomedical implants, and energy-harvesting devices. By integrating AI with structural health monitoring, adaptive control systems, and Internet of Things frameworks, smart structures evolve into intelligent entities capable of real-time diagnostics, predictive maintenance, and autonomous adaptation, as evidenced in case studies of vibration-damping skyscrapers and self-healing materials. Yet, acknowledging persistent hurdles like scalability constraints, manufacturing precision, and interdisciplinary silos, the discussion ventures into emerging trends including multi-scale modeling, sustainable hybrid designs, and explainable AI, while pinpointing research gaps in data standardization and model interpretability that beckon innovative pursuits. Ultimately, this synthesis not only underscores AI's pivotal role in unlocking metamaterials' and smart structures' untapped potential for transformative engineering solutions but also calls for collaborative advancements to forge a future of sustainable, resilient technologies that redefine human ingenuity in tackling global challenges.

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