Mechanical Characterization Using Digital Image Correlation

使用数字图像相关性进行机械表征:*纤维复合层压板

材料检测与分析技术

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作      者
出  版 社
出版时间
2021年10月09日
装      帧
精装
ISBN
9783030840396
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页      码
398
语      种
英文
版      次
2022
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图书简介
In this book, a precise treatment of the experimental characterization of advanced composite materials using Digital Image Correlation (DIC) is presented. The text explains test methods, testing setup with 2D- and stereo-DIC, specimen preparation and patterning, testing analysis and data reduction schemes to determine and to compare mechanical properties, such as modulus, strength and fracture toughness of advanced composite materials. Sensitivity and uncertainty studies on the DIC calculated data and mechanical properties for a detailed engineering-based understanding are covered instead of idealized theories and sugarcoated results. The book provides students, instructors, researchers and engineers in industrial or government institutions, and practitioners working in the field of experimental/applied structural mechanics of materials a myriad of color figures from DIC measurements for better explanation, datasets of material properties serving as input parameters for analytical modelling, raw data and computer codes for data reduction, illustrative graphs for teaching purposes, practice exercises with solutions provided online and extensive references to the literature at the end of each stand-alone chapter.Emphasizes practical matters, such as preparation and testing of specimens, design of DIC measurements, and data reduction methodologies;Compares and discusses many tensile, shear, flexure and fracture test methods that are ASTM, ISO, and DIN standards from direct and real non-idealized measurements;Provides detailed insight into testing conditions including metrological uncertainty and sensitivity studies;Reinforces concepts with photomechanical visualizations, key conclusions/good practice suggestions, and end of chapter practice exercises;Illustrates, visualizes, and explains experimental mechanics with DIC for an interdisciplinary understanding for experimentalists and modelers.
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