Comprehensive characterization, optimization, modelling, and validation for FDM of commercial PLA + for bone scaffolds

Abstract Bone scaffolds require tailored mechanical and structural properties to support tissue regeneration. This study optimizes fused deposition modeling (FDM)-printed commercial poly-lactic acid plus (PLA +) scaffolds enhanced with calcium carbonate (CaCO₃) and barium sulfate (BaSO₄) by evaluating printing temperature (190–230 °C), infill density (20–60%), and raster angle (30°–90°). Using response surface methodology (RSM) and analysis of variance (ANOVA), infill density was identified as the most statistically significant parameter ( p < 0.05), followed by raster angle and temperature. Optimal parameters (230 °C, [0,90]° raster angle, 60% infill density) achieved an elastic modulus of 1537 MPa and compressive strength of 128 MPa, surpassing conventional PLA scaffolds. Artificial neural networks (ANN) outperformed RSM in predictive accuracy, with single-output ANN models yielding high correlation coefficients (training, testing, validation). Differential scanning calorimetry (DSC) confirmed elevated crystallinity at 230 °C, while X-ray diffraction (XRD) identified semi-crystalline PLA, presence of BaSO 4 and CaCO 3 , and crystallinity shifts under compression. Fourier transform infrared spectroscopy (FTIR) revealed molecular interactions linked to strength variations, and thermogravimetric analysis (TGA)/derivative thermogravimetry (DTG) demonstrated improved thermal stability with CaCO₃/BaSO₄ additives (Weight loss: 70–74% at ≤ 210 °C vs. 92.21% at 230 °C). Scanning electron microscopy (SEM) and XRD correlated crystallinity with mechanical performance. By integrating ANN with RSM-driven validation, this work advances predictive modeling for FDM-based scaffold design, positioning commercial PLA + as a candidate for patient-specific bone tissue engineering.