Tănase Maria, Portoacă Alexandra Ileana, Diniță Alin, Brănoiu Gheorghe, Zamfir Florin, Sirbu Elena-Emilia, Călin Cătălina
Mechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.
Petroleum Geology and Reservoir Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.
Polymers (Basel). 2024 Nov 24;16(23):3268. doi: 10.3390/polym16233268.
Increasing environmental concerns and the need for sustainable materials have driven a focus towards the utilization of recycled polylactic acid (PLA) in additive manufacturing as PLA offers advantages over other thermoplastics, including biodegradability, ease of processing, and a lower environmental impact during production. This study explores the optimization of the mechanical properties of recycled PLA parts through a combination of experimental and machine learning approaches. A series of experiments were conducted to investigate the impact of various processing parameters, such as layer thickness and infill density, as well as annealing conditions, on the mechanical properties of recycled PLA parts. Machine learning algorithms have proven the possibility to predict tensile behavior with an average error of 6.059%. The results demonstrate that specific combinations of processing parameters and post-treatment annealing differently improve the mechanical properties (with 7.31% in ultimate tensile strength (UTS), 0.28% in Young's modulus, and 3.68% in elongation) and crystallinity (with 22.33%) of recycled PLA according to XRD analysis, making it a viable alternative to virgin PLA in various applications such as sustainable packaging solutions, including biodegradable containers, clamshell packaging, and protective inserts. The optimized recycled PLA parts exhibited mechanical properties and crystallinity levels comparable to those of their virgin counterparts, highlighting their potential for reducing environmental impact and saving costs. For both as-built and annealed samples, the optimal settings for achieving high composite desirability involved a 0.2 mm layer thickness, with 75% infill for the as-built samples and 100% infill for the annealed samples. This study provides a comprehensive framework for optimizing recycled PLA in additive manufacturing, contributing to the advancement of sustainable material engineering and the circular economy.
日益增长的环境问题以及对可持续材料的需求，促使人们将注意力集中在增材制造中对回收聚乳酸（PLA）的利用上，因为PLA相较于其他热塑性塑料具有诸多优势，包括生物可降解性、易于加工以及生产过程中较低的环境影响。本研究通过实验和机器学习方法相结合，探索回收PLA零件机械性能的优化。进行了一系列实验，以研究各种加工参数（如层厚和填充密度）以及退火条件对回收PLA零件机械性能的影响。机器学习算法已证明能够以6.059%的平均误差预测拉伸行为。结果表明，根据X射线衍射分析，加工参数和后处理退火的特定组合以不同方式改善了回收PLA的机械性能（极限拉伸强度（UTS）提高7.31%，杨氏模量提高0.28%，伸长率提高3.68%）和结晶度（提高22.33%），使其在各种应用（如可持续包装解决方案，包括可生物降解容器、翻盖包装和保护衬垫）中成为原生PLA的可行替代品。优化后的回收PLA零件展现出与原生零件相当的机械性能和结晶度水平，凸显了其在减少环境影响和节约成本方面的潜力。对于成型和退火后的样品，实现高综合合意性的最佳设置包括0.2毫米的层厚，成型样品的填充率为75%，退火样品的填充率为100%。本研究为增材制造中优化回收PLA提供了一个全面的框架，有助于可持续材料工程和循环经济的发展。
