Hybrid additive manufacturing and data-guided design optimization for graded anterior cruciate ligament engineering.

Interface tissues, such as the enthesis connecting ligaments to bone, present multiphasic architectures with continuous gradients in structure, composition, and mechanics. Engineering such complex transitions remains a major challenge in biofabrication. This study aims to develop a hybrid manufacturing and machine learning (ML)-guided design strategy to create functionally graded scaffolds for anterior cruciate ligament (ACL) reconstruction. A hybrid biofabrication platform was used to integrate extrusion-based three-dimensional printing and electrospinning within a single workflow. Polycaprolactone was used as the common biomaterial for both modalities. Four scaffold designs, varying in electrospun midsection length, slit patterning, and core geometry, were fabricated to replicate the native ACL's zonal architecture. Scaffolds were characterized through scanning electron microscopy (SEM) and uniaxial tensile testing. Resulting data were used to train a ML model to predict mechanical performance from geometric features. The model was then used to generate a fifth scaffold design optimized for enhanced performance. The hybrid process successfully fabricated multiscale scaffolds with integrated bone-like, enthesis-like, and ligament-like regions. SEM confirmed morphological integration between printed and electrospun structures. Mechanical testing revealed design-dependent variations in strength and stiffness. The ML model identified slit number and outer diameter as key predictors and guided the design of an optimized scaffold that combined the compliance of slitted geometries with enhanced mechanical strength. The ML-optimized scaffold achieved the highest tensile force among the slitted designs and improved stiffness compared to the other slitted configurations this study demonstrates a predictive and performance-driven biofabrication strategy that integrates hybrid additive manufacturing and ML. The approach enables rational scaffold optimization, reduces empirical iterations, and supports the development of biomimetic constructs for soft-to-hard tissue engineering. While focused on ACL reconstruction, the workflow is adaptable to a wide range of tissue interfaces.