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This study introduces a novel approach to 4D printing of biocompatible Poly lactic acid (PLA)/poly methyl methacrylate (PMMA) blends using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). The goal is to optimize PMMA content, nozzle temperature, raster angle, and printing speed to enhance shape memory properties and mechanical strength. The materials, PLA and PMMA, are melt-blended and 4D printed using a pellet-based 3D printer. Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Thermal Analysis (DMTA) assess the thermal behavior and compatibility of the blends. The ANN model demonstrates superior prediction accuracy and generalization capability compared to the RSM model. Experimental results show a shape recovery ratio of 100% and an ultimate tensile strength of 65.2 MPa, significantly higher than pure PLA. A bio-screw, 4D printed with optimized parameters, demonstrates excellent mechanical properties and shape memory behavior, suitable for biomedical applications such as orthopaedics and dental implants. This research presents an innovative method for 4D printing PLA/PMMA blends, highlighting their potential in creating advanced, high-performance biocompatible materials for medical use.
Doostmohammadi et al. (2024) studied this question.