Data-Driven Discovery of Structure–Performance Relationships in Bio-Inspired Engineering Architectures for Lightweight Design

Nature has evolved highly efficient structural systems that achieve exceptional mechanical performance while minimizing material consumption. Biological architectures such as trabecular bone, honeycomb networks, coral structures, and spider webs have inspired the development of lightweight engineering designs for aerospace, automotive, and advanced manufacturing applications. However, most existing studies focus on individual bio-inspired architectures, making it difficult to establish a unified understanding of their relative performance and application potential. This study proposes a data-driven framework for investigating structure–performance relationships in bio-inspired engineering architectures using machine learning and literature-derived datasets. Mechanical performance indicators, including relative density, specific stiffness, specific strength, energy absorption, and weight-reduction efficiency, are collected from published experimental and numerical studies covering multiple biological design inspirations. Unsupervised machine learning techniques are employed to identify performance clusters, reveal hidden similarities among architecture families, and establish quantitative relationships between structural topology and engineering performance. A multidimensional performance atlas is further developed to visualize and compare diverse bio-inspired architectures within a unified design space. The novelty of this work lies in the development of a machine learning-guided comparative framework that transforms fragmented literature data into an interpretable engineering decision-support tool. Unlike conventional review studies that provide qualitative assessments, the proposed approach enables quantitative comparison of multiple bio-inspired architectures using a common set of performance metrics. The resulting framework provides insights into architecture-specific strengths, trade-offs, and application-oriented design opportunities, supporting the selection of optimal lightweight structures for future engineering systems. The proposed methodology offers a scalable pathway for integrating biological inspiration, data analytics, and engineering design in advanced manufacturing applications.