利用 Anova 和神经网络优化原生和回收聚乳酸部件的机械性能

Q4 Engineering
Girish Kumar P V R, Devaki Devi K
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引用次数: 0

摘要

增材制造(AM)对聚合物的需求日益增长,导致塑料废弃物大幅增加,近年来使用量已超过 3 亿吨。这篇研究文章探讨了聚乳酸(PLA)作为一种可生物降解的热塑性再生材料在 3D 打印部件中的应用,比较了其与原生聚乳酸的特性,并讨论了改进其变化和机械特性的解决方案。熔融沉积建模(FDM)是一种广泛使用的增材制造工艺,可通过逐层沉积熔融材料来创建三维物体。本研究调查了填充密度、层厚度和光栅角度对再生三维打印材料的影响,重点关注它们的尺寸及其对加工效率的影响。本研究论文旨在研究使用 FDM 结合不同工艺参数打印的再生三维打印部件与原生聚乳酸相比的机械效果。研究结果表明,最佳工艺参数可提高再生 3D 打印部件的质量和性能。随后,研究人员使用方差分析(ANOVA)作为统计工具,并使用方差网络(ANN)技术对结果进行了比较,从而最大限度地减少了误差偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing mechanical properties of virgin and recycled PLA components using Anova and neural networks
The increasing demand for polymers in additive manufacturing (AM) has led to a significant increase in plastic waste, with over 300 million metric tons used in recent years. This research article explores the use of Poly Lactic Acid (PLA) as a biodegradable thermoplastic recycled material for 3D printed components, comparing its properties with virgin PLA and discussing solutions for variation and mechanical features improvement. Fused Deposition Modeling (FDM) is a widely used additive manufacturing process that allows for the creation of three-dimensional objects by depositing molten material layer by layer. This study investigates the impact of infill density, layer thickness, and raster angle for recycled 3D printing material, focusing on their dimensions and their influence on processing efficiency. This research paper aims to investigate the mechanical effects of recycled 3d printed components which are printed by using FDM with the combination of different process parameters compared with virgin PLA. From results optimal process parameters are found to enhancing quality and performance of recycled 3D printed components. Later results are compared by Analysis of Variance (ANOVA) as a statistical tool and also with ANN technique, which minimizes error deviation.
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CiteScore
0.10
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10 weeks
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