Machine Learning-Based Design for Additive Manufacturing in Biomedical Engineering

While ceramic additive manufacturing (AM) technologies have shown great promise to create functional scaffolds with tailored biomechanical properties, the true potential of these advanced techniques has not been fully exploited yet due to lack of practical optimisation design approaches. To address this challenge, a machine learning (ML)-based design approach is proposed herein where ceramic 3D printing techniques are combined to fabricate functionally graded scaffolds composed of Triply Periodic Minimal Surfaces (TPMS), aiming to fulfil the anticipated biomechanical requirements of the target bone regeneration outcomes. The proposed ML based design strategy couples a Bayesian optimisation (BO) algorithm to enable time-dependent mechano-biological optimisation of the 3D printed ceramic scaffolds at a reasonably low computational cost. For a representative example relating to bone scaffolding in a segmental defect of sheep tibia, the simulated results demonstrate that the optimised functionally graded scaffolds significantly enhance bone ingrowth outcomes. Furthermore, a Lithography-based Ceramic Manufacturing (LCM) technique is employed to fabricate the optimised scaffolds based on the proposed ML-based design framework, followed by micro-CT analyses of the additively manufactured ceramic scaffolds to assess their geometric qualities. This study is expected to gain new insights into mechanical sciences on design for varying material conditions and provide an effective design tool for ceramic additive manufacturing.