Process Modeling and Micromolding Optimization of HA- and TiO₂-Reinforced PLA/PCL Composites for Cannulated Bone...

A bioresorbable cannulated bone screw was developed using PLA/PCL-based composites reinforced with hydroxyapatite (HA) and titanium dioxide (TiO₂), two additives previously reported to enhance mechanical compliance, biocompatibility, and molding feasibility in biodegradable polymer systems. The design incorporated a crest-trimmed thread and a strategically positioned gate in the thin-wall zone opposite the hexagonal socket to preserve torque-transmitting geometry during micromolding. To investigate shrinkage behavior, a Taguchi orthogonal array was employed to systematically vary micromolding parameters, generating a structured dataset for training a back-propagation neural network (BPNN). Analysis of variance (ANOVA) identified melt temperature as the most influential factor affecting shrinkage quality, defined by a combination of shrinkage rate and dimensional variation. A hybrid AI framework integrating the BPNN with genetic algorithms and particle swarm optimization (GA-PSO) was applied to predict the optimal shrinkage conditions. This is the first use of BPNN-GA-PSO for cannulated bone screw molding, with the shrinkage rate as a targeted output. The AI-predicted solution, interpolated within the Taguchi design space, achieved improved shrinkage quality over all nine experimental groups. Beyond the specific PLA/PCL-based systems studied, the modeling framework-which combines geometry-specific gate design and normalized shrinkage prediction-offers broader applicability to other bioresorbable polymers and hollow implant geometries requiring high-dimensional fidelity. This study integrates composite formulation, geometric design, and data-driven modeling to advance the precision micromolding of biodegradable orthopedic devices.