PSI - Issue 28

Anja Gosch et al. / Procedia Structural Integrity 28 (2020) 1184–1192 Anja Gosch/ Structural Integrity Procedia 00 (2019) 000–000

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1. Introduction Accurate lifetime predictions of components require precise knowledge of the fracture behavior of the material used. One way to describe the fracture behavior is the use of crack growth kinetic curves. These curves display the crack growth rate d  a/dN (crack extension  a per cycle N) depending on the applied stress intensity factor range  K in a double logarithmic plot. They are independent of the geometry and configuration of the specimen. Based on these curves, lifetime prediction of components can be performed.

Nomenclature A

fitting parameter of power law fit for lifetime estimation

CT DIC IRT

compact tension (specimen type) digital image correlation

infrared thermography

m

fitting parameter of power law fit for lifetime estimation

m p plastic constraint factor PVC-U unplasticised Polyvinylchloride POM Polyoxymethylene PMMA Polymethylmethacrylate r pl plastic zone radius  K stress intensity factor range d  a/dN crack extension  a per cycle N  y yield stress

The accurate and reliable measurement of the crack growth during the fatigue test is one of the most vital aspects for component design based on crack growth kinetics curves (Lang et al. , 2004; Hertzberg and Manson, 1980; Lang, 1984; Berer and Pinter, 2013). However, in the case of polymers, the crack length detection can be quite challenging. It is still state of the art to measure the crack advancement during fatigue measurements of polymers manually, by the use of a travelling microscope coupled with a linear displacement transducer. For measurements via the travelling microscope, the fatigue test has to be stopped for each crack length measurement, which limits the number of data points, potentially influences the test results themselves, and is influenced by the user. Hence, the crack length detection via travelling microscope is time-consuming and subjectively based on the perception of the user (Berer and Pinter, 2013). This makes the procedure unattractive for the lifetime estimation in engineering applications. There are two standards for polymers, ISO 15850 and ASTM E 647-11, which use a linear elastic material behavior to evaluate the crack length during testing via the changing compliance. In this case, the machine data can be used to calculate the specimen compliance during crack growth, as described in literature (Berer and Pinter, 2013; International Standard ISO 15850, 2002b; Saxena and Hudak, 1978). However, for polymers with a highly viscoelastic material behavior this procedure is not directly applicable, because it neglects the time-dependent properties of polymers. There are further methods, which are combining the compliance evaluation with optical measurements (Berer and Pinter, 2013; Novotny, 1997). This increases the amount of available data points, but optical measurements of the crack length are still necessary. Hence, the automation of the crack length detection is of great interest to push lifetime estimation of polymer components towards an accurate, objective and automated procedure. Thus the aim of this work was to develop methods for the automated crack length detection of polymers and to verify the chosen methods for different polymer types. Two measuring techniques were examined regarding their general applicability in fracture mechanical fatigue tests: (i) the infrared thermography (IRT) and (ii) the digital image correlation (DIC). The IRT method offers the opportunity to locate the crack tip during fracture mechanical fatigue testing, since the temperature of the specimen rises in the area of the crack tip. The rising temperature close to the crack tip is attributed to hysteretic heating caused by cyclic energy dissipation (Anderson, 2005). Hence, the IRT method is a promising tool to locate the crack tip on the specimen surface. Additionally, a DIC-based method, which provides full-field information about the surface deformation on an object, was used (Yoneyama, 2016; Wigger et al. , 2018). The DIC enables to describe the longitudinal and transverse strain distribution on the specimen surface. Hence, it is possible to gain information