Computational Design Strategies and Software for Lattice Structures and Functionally Graded Materials

Abstract

This study presents a comparative analysis of software platforms and computational methods used in the design of three-dimensional lattice structures and functionally graded materials (FGMs). Through systematic evaluation of 31 computational platforms across seven critical criteria (lattice type support, parametric control, conformal generation, multi-material capabilities, ease of use, FEA integration, and AM compatibility), this review identifies that specialized platforms significantly outperform general-purpose CAD tools, with scores exceeding 30/35 points compared to 15–20/35 for conventional systems. The analysis reveals that implicit and voxel-based representations dominate high-performance applications, while traditional boundary-representation methods approach fundamental limitations for complex lattice generation. Emerging machine learning-driven frameworks demonstrate 82% reduction in optimization iterations through Bayesian optimization and achieve property prediction speedups of nearly 100× compared to computational homogenization, enabling rapid inverse design workflows previously computationally infeasible. These insights provide researchers with evidence-based guidance for selecting computational approaches aligned with specific manufacturing capabilities and design objectives.