Maximizing performance and efficiency in 3D printing of polylactic acid biomaterials: Unveiling of microstructural morphology, and implications of process parameters and modeling of the mechanical strength, surface roughness, print time, and print energy for fused filament fabricated (FFF) bioparts

聚乳酸 极限抗拉强度 填充 抗弯强度 材料科学 表面粗糙度 熔丝制造 复合材料 3D打印 表面光洁度 复合数 结构工程 聚合物 工程类
作者
Ray Tahir Mushtaq,Li Wang,Chengwei Bao,Mudassar Rehman,Shubham Sharma,Aqib Mashood Khan,ElSayed M. Tag El-Din,Mohamed Abbas
出处
期刊:International Journal of Biological Macromolecules [Elsevier]
卷期号:259: 129201-129201 被引量:9
标识
DOI:10.1016/j.ijbiomac.2024.129201
摘要

Medical stents, artificial teeth, and grafts are just some of the many applications for additive manufacturing techniques like bio-degradable polylactic acid 3D printing. However, there are drawbacks associated with fused filament fabrication-fabricated objects, including poor surface quality, insufficient mechanical strength, and a lengthy construction time for even a relatively small object. Thus, this study aims to identify the finest polylactic acid 3D printing parameters to maximize print quality while minimizing energy use, print time, flexural and tensile strengths, average surface roughness, and print time, respectively. Specifically, the infill density, printing speed, and layer thickness are all variables that were selected. A full-central-composite design generated 20 samples to test the prediction models' experimental procedures. Validation trial tests were used to show that the experimental findings agreed with the predictions, and analysis of variance was used to verify the importance of the performance characteristics (ANOVA). At layer thickness = 0.26 mm, infill density = 84 %, and print speed = 68.87 mm/s, the following optimized values were measured for PLA: flexural strength = 70.1 MPa, tensile strength = 39.2 MPa, minimum surface roughness = 7.8 μm, print time = 47 min, and print energy = 0.18 kwh. Firms and clinicians may benefit from utilizing the developed, model to better predict the required surface characteristic for various aspects afore trials.

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