School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
J Mech Behav Biomed Mater. 2024 Dec;160:106719. doi: 10.1016/j.jmbbm.2024.106719. Epub 2024 Sep 4.
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.
本研究提出了一种使用人工神经网络 (ANN) 和响应面法 (RSM) 对生物相容性聚乳酸 (PLA)/聚甲基丙烯酸甲酯 (PMMA) 共混物进行 4D 打印的新方法。目标是优化 PMMA 含量、喷嘴温度、栅格角度和打印速度，以增强形状记忆性能和机械强度。使用基于颗粒的 3D 打印机对 PLA 和 PMMA 材料进行熔融共混和 4D 打印。差示扫描量热法 (DSC) 和动态机械热分析 (DMTA) 评估了共混物的热行为和相容性。与 RSM 模型相比，ANN 模型显示出更高的预测精度和泛化能力。实验结果表明，形状恢复率为 100%，极限拉伸强度为 65.2 MPa，明显高于纯 PLA。用优化参数 4D 打印的生物螺钉具有优异的机械性能和形状记忆性能，适用于矫形和牙科植入等医学应用。本研究提出了一种用于 PLA/PMMA 共混物 4D 打印的创新方法，突出了它们在为医疗用途创造先进、高性能生物相容性材料方面的潜力。
