Work

Intelligent generative design: a transfer learning-enhanced bidirectional mapping framework for customised parametric structural design

Year: 2025

Type: article

Abstract: Tissue engineering scaffolds with tailored mechanical properties are crucial for regenerative medicine. However, establishing the intricate relationships between scaffold design parameters and resulting mechanical behaviour remains a challenge. Here, we present a transferable, machine-learning-driven framework for the rapid customisation of triply periodic minimal surface (TPMS) scaffolds. We employ a genetic algorithm to optimise a back-propagation neural network (GA-BPNN) for forward prediction of elastic modulus from structural parameters (porosity, pore size). Transfer learning enables efficient adaptation of the model to different TPMS topologies (Primitive, Gyroid, Diamond, I-Wrapped Package) with minimal retraining. Furthermore, we develop a BPNN-SCLFPSO (synchronous changing learning factor particle swarm optimisation) reverse search model, allowing for the inverse design of TPMS structures with targeted mechanical properties. Simulations demonstrate high predictive accuracy (R2 > 0.97) across diverse material compositions. Physical validation via additive manufacturing and compression testing confirms the model's reliability, with average relative errors of 7.6% and 4.8% for PCU and Ti alloy, respectively. This framework offers a powerful tool for on-demand design of TPMS scaffolds, accelerating the development of personalised tissue engineering solutions.

Source: Virtual and Physical Prototyping

DOI: 10.1080/17452759.2025.2510001