Sunny Narayan, Brahim Menacer, Muhammad Usman Kaisan, Joseph Samuel, Moaz Al-Lehaibi, Faisal O Mahroogi, Víctor Tuninetti
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A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. 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引用次数: 0
摘要
仿生晶格结构的灵感来自于自然结构,如骨头、珊瑚、软体动物壳和曲霉,由于其特殊的强度重量比、能量吸收和变形控制而受到越来越多的关注。这些特性使其成为航空航天、生物医学设备和结构冲击防护等先进工程应用的理想选择。本研究对2020年至2025年间发表的全球仿生晶格结构研究进行了全面的文献计量分析,旨在确定主题趋势、合作模式和未开发领域。从PubMed、Dimensions、Scopus、IEEE、b谷歌Scholar和Science Direct等数据库中提取了一个包含3685份出版物的精心策划的数据集,并将其合并在一起。在删除重复和清理后,使用Cite space, VOS viewer version 1.6.20和Bibliometrix R软件包(4.5),选择了大约2226篇完整的研究论文进行文献计量学分析,不包括综述作品,会议论文,书籍章节和笔记。64位),用于绘制合作作者网络、机构隶属关系、关键词共现和引文关系。在过去一年中,出版物的数量显著增加,反映出人们对这一领域的兴趣日益浓厚。结果表明,中国是最多产的贡献者,拥有大量的机构支持和活跃的合作网络,特别是与欧洲研究小组的合作。重点研究方向包括增材制造、有限元建模、基于机器学习的设计优化以及生物启发几何的性能评估。值得注意的是,将人工智能集成到结构建模中正在加速向数据驱动设计框架的转变。然而,在几何建模标准化、疲劳行为分析和复杂载荷条件下晶格结构的实际验证方面仍然存在差距。本研究对当前的研究方向进行了战略性概述,并为未来的跨学科探索提供了指导。这些见解旨在支持研究人员和实践者推进具有卓越机械性能和特定应用适应性的下一代仿生材料。
Global Research Trends in Biomimetic Lattice Structures for Energy Absorption and Deformation: A Bibliometric Analysis (2020-2025).
Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability.