{"title":"Recent advancements in morphing applications: Architecture, artificial intelligence integration, challenges, and future trends-a comprehensive survey","authors":"Md. Najmul Mowla , Davood Asadi , Tahir Durhasan , Javad Rashid Jafari , Mohammadreza Amoozgar","doi":"10.1016/j.ast.2025.110102","DOIUrl":null,"url":null,"abstract":"<div><div>This study provides a comprehensive review of recent advancements in aerospace morphing technologies, focusing on integrating artificial intelligence (AI) into morphing architectures. It emphasizes AI's pivotal role in optimizing these systems, particularly through machine learning (ML), deep learning (DL), and reinforcement learning (RL), to enhance real-time adaptability, performance, and efficiency. The review categorizes developments in smart materials, compliant mechanisms, and adaptive structures, offering a detailed analysis of their architectural foundations. It further examines AI-driven aerodynamic optimization and control systems, highlighting recent solutions to structural integrity, energy efficiency, and scalability challenges. Key contributions since 2020 are synthesized through a year-by-year analysis, offering a clear overview of the research landscape. The paper also addresses emerging challenges in aerospace morphing and proposes strategies to alleviate them. Recommendations for future advancements emphasize the integration of state-of-the-art technologies. By critically evaluating current capabilities and limitations, this review provides valuable insights for researchers and practitioners, identifying AI's transformative potential in morphing systems and outlining the technical challenges that must be addressed for future morphing aerospace applications.</div></div>","PeriodicalId":50955,"journal":{"name":"Aerospace Science and Technology","volume":"161 ","pages":"Article 110102"},"PeriodicalIF":5.0000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerospace Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1270963825001737","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
This study provides a comprehensive review of recent advancements in aerospace morphing technologies, focusing on integrating artificial intelligence (AI) into morphing architectures. It emphasizes AI's pivotal role in optimizing these systems, particularly through machine learning (ML), deep learning (DL), and reinforcement learning (RL), to enhance real-time adaptability, performance, and efficiency. The review categorizes developments in smart materials, compliant mechanisms, and adaptive structures, offering a detailed analysis of their architectural foundations. It further examines AI-driven aerodynamic optimization and control systems, highlighting recent solutions to structural integrity, energy efficiency, and scalability challenges. Key contributions since 2020 are synthesized through a year-by-year analysis, offering a clear overview of the research landscape. The paper also addresses emerging challenges in aerospace morphing and proposes strategies to alleviate them. Recommendations for future advancements emphasize the integration of state-of-the-art technologies. By critically evaluating current capabilities and limitations, this review provides valuable insights for researchers and practitioners, identifying AI's transformative potential in morphing systems and outlining the technical challenges that must be addressed for future morphing aerospace applications.
期刊介绍:
Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to:
• The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites
• The control of their environment
• The study of various systems they are involved in, as supports or as targets.
Authors are invited to submit papers on new advances in the following topics to aerospace applications:
• Fluid dynamics
• Energetics and propulsion
• Materials and structures
• Flight mechanics
• Navigation, guidance and control
• Acoustics
• Optics
• Electromagnetism and radar
• Signal and image processing
• Information processing
• Data fusion
• Decision aid
• Human behaviour
• Robotics and intelligent systems
• Complex system engineering.
Etc.