A Methodology to Model the Statistical Fracture Behavior of Acrylic Glasses for Stochastic Simulation
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Beschreibung
In this work, a methodology is proposed to statistically model the fracture behavior of acrylic glasses for numerical simulation. The aim is to give guidance to users of comparable materials. In detail, the collection of necessary measurement data and their analysis is presented. By applying the model, a stochastic finite-element simulation is performed for a head impact onto an automotive rear-side window made from acrylic glass. Based on the wide spread of the resulting head injury criteria, the relevance of a statistical material characterization for product safety is discussed.
Marcel Berlinger studied mechanical engineering at Technische Hochschule Mittelhessen – University of Applied Sciences, and completed his Master's degree in 2017. Since then, he has been a research assistant and doctoral student in the Team of Prof. Dr.-Ing. habil. Stefan Kolling at the Institute of Mechanics and Materials at Technische Hochschule Mittelhessen.
A statistical modelling method for simulating the fracture behaviour of acrylic glass is presented using the application case of an automotive rear side window. The collection of the necessary measurement data and their analysis is described. The aim is to give guidance to users of comparable materials. Finally, the model is integrated into the finite element simulation of a head impact test of the pane. Based on the resulting spread of the head injury criterion, the relevance of a statistical material characterisation for product safety is discussed.
Application-related introduction to the basics of statistics Sensitisation for stochastic effects Guidance on the design of safety-relevant polymer components
Autor*in
Marcel Berlinger
Themen in »A Methodology to Model the Statistical Fracture Behavior of Acrylic Glasses for Stochastic Simulation«
Fundamentals Generalized Anderson-Darling Test Experimental Investigations Sampling Statistical Modeling Stochastic Simulation Experimental Basis for Model Enhancements