首页|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)
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)
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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."
NASA John H. Glenn Research CenterClev elandOhioUnited StatesNorth and Central AmericaCyborgsEmerging Technol ogiesMachine Learning
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