A Multi-Scale Computational Framework for Bio-Inspired Hierarchical Design of Recycled Thermoset DLP Composites

Abstract

Digital Light Processing (DLP) additive manufacturing generates substantial thermoset waste, yet no computational framework exists to guide hierarchical design of recycled DLP composites toward bio-inspired mechanical performance. This work presents a multi-scale framework integrating four coupled modules: a cure-state-dependent physics engine combining the DiBenedetto model, Mori-Tanaka homogenisation, Classical Laminate Theory, and Timoshenko shear correction; Buckingham Pi dimensional analysis; physics-engine-derived synthetic data generation using Latin Hypercube Sampling with Gaussian process residual correction; and dual-target Random Forest and XGBoost models validated by leave-one-out cross-validation against 18 experimental formulations from a recycled Monocure 3D Rapid Epoxy composite system. Cure state was confirmed as the dominant driver of flexural stiffness (3.40× modulus ratio between 30- and 60-minute post-cure). The framework achieved R2 = 0.949 and MAPE = 12.7% for flexural modulus, with XGBoost achieving MAPE = 6.4% for flexural strength; interface compliance emerged as the highest-importance strength feature. Bio-mimicry scoring across 2,000 feasible designs revealed all candidates cluster near the wood bio-target, with bone and nacre unreachable at the near-unity modulus contrast (Ep/Em ≈ 1.2). Hollow cross-section architecture delivered +56% flexural stiffness-to-mass improvement at 40% mass reduction. The framework identifies 600 high-priority designs for validation and establishes structural geometry as the primary lever for advancing recycled thermoset composites toward bio-inspired performance.

Keywords: Bio-inspired hierarchical composites, Digital Light Processing, Recycled thermoset composites, Physics-informed machine learning, Multi-scale homogenisation, Dimensional analysis