Bio-inspired geometric modeling with texture-driven surface generation based on parametric framework for additive manufacturing

Bio-inspired geometries provide efficient and multifunctional design solutions; however, translating these complex natural forms into manufacturable digital models remains a significant challenge within conventional Computer-Aided Design workflows. This study proposes a parametric geometric modeling framework for generating bio-inspired geometries with integrated surface textures specifically intended for additive manufacturing. The proposed methodology consists of three sequential stages: (1) generation of biomimetic geometries from mathematical parametric surfaces, (2) creation of complex texture patterns through parametric formulations, and (3) integration of both components into a unified high-resolution geometric model suitable for fabrication. The framework is validated through representative case studies, including the development of a bio-inspired motorboat hull and multiple textured biomimetic surfaces. The resulting geometries were computationally generated, implemented in Python code, and successfully manufactured using fused deposition modeling, demonstrating the capability of the proposed approach to efficiently generate diverse design alternatives while preserving geometric fidelity and manufacturability. Unlike conventional sequential modeling approaches, the proposed method integrates global shape generation and local surface texture within a single mathematical formulation, enabling rapid morphological exploration during conceptual design and supporting Design for Additive Manufacturing. The proposed framework contributes to computational design by providing a unified mathematical strategy for texture-driven bio-inspired geometric modeling that expands the design space while reducing the complexity associated with traditional CAD modeling of organic geometries.