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Process optimization and stochastic modeling of void contents and mechanical properties in additively manufactured composites

机译:增材制造复合材料中空隙率和机械性能的工艺优化和随机建模

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摘要

The effects of process parameters (print temperature, bed temperature, and print speed) on dimensional accuracy, void contents, and mechanical properties are experimentally investigated for the additively manufactured short carbon fiber reinforced composites. Such studies are carried out at bead, lamina, and laminate levels to identify the process-microstructure-property relationship. Different dimensional parameters such as height, width, and deposition-alignment are monitored for different process parameters at the bead level and its effects on lamina, and laminate are studied in a multi-level manner. The various sources of uncertainties in fused filament fabrication (FFF) based additive manufacturing (AM) are identified, and their adverse effects on microstructure and performance are analyzed. Utilizing the experimentally extracted data for dimensional variability and mechanical property at each level, physical models were adopted to accurately quantify the uncertainty. A non-intrusive polynomial chaos (NIPC) based uncertainty analysis was introduced to improve the computational efficiency and reliability of the physical models. The classical lamination theory (CLT) is used with a slight modification to account for different kinds of voids of short fiber reinforced composites manufactured by FFF. The adjusted process parameters for 5% carbon fiber reinforced polylactic acid (CF/PLA) composite showed minimum dimensional variability and maximum structural performance at a print temperature of 220 degrees C, bed temperature of 80 degrees C, and print speed of 20 mm /s. The maximum dimensional accuracy, minimum void contents, and improved mechanical properties support these optimized processed parameters. These optimized parameters may be related to the viscosity of the material to identify the same parameters for other material systems. The NIPC accurately predicted infra-bead, inter-bead and interfacial-bead voids contents with the overall void contents in the range of 20-26%. All these predicted void contents were incorporated in the CLT to predict the stochastic load distribution of the laminate. The stochastic model closely predicted the laminate properties in terms of axial load distributions which were validated by experimentation of [0 degrees](s), [90 degrees](s), [45 degrees](s), [0 degrees/90 degrees](s), and [+/- 45 degrees](s) laminate testing.
机译:对于添加制造的短碳纤维增强复合材料,通过实验研究了工艺参数(印刷温度,床温和印刷速度)对尺寸精度,空隙含量和机械性能的影响。此类研究在焊珠,薄层和层压板级别进行,以识别工艺-微结构-性能的关系。在珠子级别监视不同尺寸参数(例如高度,宽度和沉积对齐)的不同工艺参数,以及其对薄片的影响,并以多级方式研究层压板。确定了基于熔丝制造(FFF)的增材制造(AM)的各种不确定性来源,并分析了它们对微观结构和性能的不利影响。利用实验提取的数据在每个级别上的尺寸变化和机械性能,采用物理模型来准确地量化不确定性。引入了基于非侵入式多项式混沌(NIPC)的不确定性分析,以提高物理模型的计算效率和可靠性。使用经典层压理论(CLT)进行了一些修改,以说明FFF制造的短纤维增强复合材料的不同类型的空隙。调整后的5%碳纤维增强聚乳酸(CF / PLA)复合材料的工艺参数在印刷温度为220摄氏度,床温为80摄氏度,印刷速度为20毫米/秒的情况下显示出最小的尺寸变异性和最大的结构性能。最高的尺寸精度,最小的空隙含量和改善的机械性能支持这些优化的加工参数。这些优化的参数可能与材料的粘度有关,以标识其他材料系统的相同参数。 NIPC准确预测了珠下,珠间和界面珠的空隙含量,总空隙含量在20%至26%的范围内。将所有这些预测的空隙含量合并到CLT中,以预测层压板的随机载荷分布。随机模型根据轴向载荷分布紧密地预测了层压板的性能,通过[0度],[90度],[45度],[0度/ 90度]的实验进行了验证]和[+/- 45度]层压板测试。

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