Robotics & Machine Learning Daily News2024,Issue(Jun.25) :11-11.

New Machine Learning Study Findings Have Been Published by a Researcher at NASA John H. Glenn Research Center (Additively Manufactured Carbon-Reinforced ABS Hon eycomb Composite Structures and Property Prediction by Machine Learning)

美国国家航空航天局约翰·H·格伦研究中心的一位研究人员发表了新的机器学习研究结果(通过机器学习附加制造的碳增强ABS Hon eycomb复合材料结构和性能预测)

Robotics & Machine Learning Daily News2024,Issue(Jun.25) :11-11.

New Machine Learning Study Findings Have Been Published by a Researcher at NASA John H. Glenn Research Center (Additively Manufactured Carbon-Reinforced ABS Hon eycomb Composite Structures and Property Prediction by Machine Learning)

美国国家航空航天局约翰·H·格伦研究中心的一位研究人员发表了新的机器学习研究结果(通过机器学习附加制造的碳增强ABS Hon eycomb复合材料结构和性能预测)

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摘要

由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于人工智能的最新研究结果已经发表。根据来自俄亥俄州克利夫兰的新闻,由News Rx记者报道,研究表明:“聚合物3D打印技术的广泛应用和对高性能轻质结构的需求促进了各种碳增强聚合物复合纤维的出现。”新闻记者从NASA John H. Glenn Re Search Center的研究中获得了一句话:“然而,这些材料的加工-微观结构-性能关系的详细表征仍然需要实现它们的全部潜力。在这项研究中,丙烯腈-丁二烯-苯乙烯(ABS)和两种Ca Rbon增强的ABS变体,要么是碳纳米管(CNT),要么是5重量%的短切碳纤维(CF)采用生物启发的蜂窝结构设计。通过熔丝制造(FFF)制造了这些结构,并在一定的层厚度和六边形(HEX)尺寸范围内进行了投资。通过材料横截面显微镜评估了Print参数和孔隙率之间的关系。分析确定了与较大的印刷层高度和HEX尺寸相比,较低的印刷层高度和HEX尺寸降低孔隙率的趋势。力学性能通过压缩试验进行评价,ABS试样获得了较高的压缩屈服强度,而cnt-ABS由于孔隙率的减少和随后的强化而获得了较高的极限抗压强度。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news originating from Cleveland, Ohio, by News Rx correspondents, research stated, "The expansive utility of polymeric 3D-print ing technologies and demand for high- performance lightweight structures has pro mpted the emergence of various carbon-reinforced polymer composite filaments." The news reporters obtained a quote from the research from NASA John H. Glenn Re search Center: "However, detailed characterization of the processing-microstruct ure-property relationships of these materials is still required to realize their full potential. In this study, acrylonitrile butadiene styrene (ABS) and two ca rbon-reinforced ABS variants, with either carbon nanotubes (CNT) or 5 wt.% chopped carbon fiber (CF), were designed in a bio-inspired honeycomb geometry. T hese structures were manufactured by fused filament fabrication (FFF) and invest igated across a range of layer thicknesses and hexagonal (hex) sizes. Microscopy of material cross-sections was conducted to evaluate the relationship between p rint parameters and porosity. Analyses determined a trend of reduced porosity wi th lower print-layer heights and hex sizes compared to larger print-layer height s and hex sizes. Mechanical properties were evaluated through compression testin g, with ABS specimens achieving higher compressive yield strength, while CNT-ABS achieved higher ultimate compressive strength due to the reduction in porosity and subsequent strengthening."

Key words

NASA John H. Glenn Research Center/Clev eland/Ohio/United States/North and Central America/Cyborgs/Emerging Technol ogies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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