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Part of the book series: Mechanik, Werkstoffe und Konstruktion im Bauwesen (MWKB, volume 52)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
A method for incorporating and comparing stochastic scatter of macroscopic parameters in crash simulations is developed in the present work and applied on a 30 wt.% short glass fiber reinforced polypropylene. Therefore, a statistical testing plan on the basis of three point bending tests with 30 samples for each configuration is carried out. The tests are conducted at 0°, 30°, 45° and 90° orientation angles and at strain rates of 0.021/s and 85/s. The obtained results are evaluated statistically by means of probability distribution functions. An orthotropic elastic plastic material model is utilized for the numerical investigations. Monte Carlo Simulations with variations in macroscopic parameters are run to emulate the stochastic rupture behavior of the experiments.
Authors and Affiliations
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Beselich, Germany
Nikolai Sygusch
About the author
Nikolai Sygusch was Research Associate at the Institute of Mechanics and Materials, Working Group Kolling, TH Mittelhessen, Gießen and has been a Ph.D. student from 2015 until 2018 at the crash simulation at Opel Automobile GmbH, Rüsselsheim am Main.
Bibliographic Information
Book Title: Stochastic Approach to Rupture Probability of Short Glass Fiber Reinforced Polypropylene based on Three-Point-Bending Tests
Authors: Nikolai Sygusch
Series Title: Mechanik, Werkstoffe und Konstruktion im Bauwesen
DOI: https://doi.org/10.1007/978-3-658-27113-8
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020
Softcover ISBN: 978-3-658-27112-1Published: 29 July 2019
eBook ISBN: 978-3-658-27113-8Published: 20 July 2019
Series ISSN: 2512-3238
Series E-ISSN: 2512-3246
Edition Number: 1
Number of Pages: X, 145
Number of Illustrations: 1 b/w illustrations
Topics: Building Materials, Building Construction and Design, Construction Management