Mechanical performance optimization in FFF 3D printing using Taguchi design and machine learning approach with PLA/walnut Shell composites filaments

IF 3.8 4区 工程技术 Q2 CHEMISTRY, APPLIED
Fuat Kartal
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引用次数: 0

Abstract

This study explores the optimization of mechanical properties in 3D-printed components made from a Polylactic Acid (PLA) and Walnut Shell Composite using Fused Filament Fabrication (FFF). Employing a machine learning-based approach, the research identifies the optimal regression model for predicting relationships between printing parameters and material properties. A Taguchi L18 design is used to minimize experiment count while accurately determining parameter levels. Testing was conducted on a composite containing 30% walnut shell fibers, with the Ultimate Tensile Strength (UTS) and Elastic Modulus (E) measured as per ASTM D638 standards. Experimental factors included Layer Thickness (LT), Nozzle Temperature (NT), Deposition Angle (DA), and Printing Speed (PS). Using Analysis of Variance (ANOVA) and machine learning techniques, the effects of these parameters on UTS and E were evaluated. Results highlight the deposition angle as the dominant parameter, with machine learning models, especially Random Forest Regression, providing highly accurate predictions. This approach presents a novel, data-driven method for optimizing 3D printing processes with sustainable, composite materials.

Highlights

  • Higher UTS and E achieved with optimized PLA/walnut shell composite.
  • Deposition angle is the key element of mechanical performance in FFF printing.
  • Layer thickness is important to improve Elastic Modulus.
  • Statistical and machine learning techniques combined for sustainable printing.
  • Improved machine learning process understanding for 3D printed components.

Abstract Image

使用田口设计和机器学习方法优化PLA/核桃壳复合材料长丝的FFF 3D打印机械性能
本研究探索了使用熔融长丝制造(FFF)技术对聚乳酸(PLA)和核桃壳复合材料制成的3d打印部件的机械性能进行优化。该研究采用基于机器学习的方法,确定了预测打印参数和材料性能之间关系的最佳回归模型。田口L18设计用于最小化实验计数,同时准确地确定参数水平。在含有30%核桃壳纤维的复合材料上进行了测试,根据ASTM D638标准测量了极限拉伸强度(UTS)和弹性模量(E)。实验因素包括层厚(LT)、喷嘴温度(NT)、沉积角(DA)和打印速度(PS)。使用方差分析(ANOVA)和机器学习技术,评估这些参数对UTS和E的影响。结果强调沉积角是主要参数,机器学习模型,特别是随机森林回归,提供了高度准确的预测。这种方法提出了一种新颖的,数据驱动的方法,用于优化可持续的复合材料3D打印过程。通过优化PLA/核桃壳复合材料实现更高的UTS和E。沉积角是影响FFF打印机械性能的关键因素。层厚是提高弹性模量的重要因素。统计和机器学习技术相结合的可持续印刷。改进了对3D打印组件的机器学习过程的理解。
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来源期刊
Journal of Vinyl & Additive Technology
Journal of Vinyl & Additive Technology 工程技术-材料科学:纺织
CiteScore
5.40
自引率
14.80%
发文量
73
审稿时长
>12 weeks
期刊介绍: Journal of Vinyl and Additive Technology is a peer-reviewed technical publication for new work in the fields of polymer modifiers and additives, vinyl polymers and selected review papers. Over half of all papers in JVAT are based on technology of additives and modifiers for all classes of polymers: thermoset polymers and both condensation and addition thermoplastics. Papers on vinyl technology include PVC additives.
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