Cyclic behavior and modeling of small fatigue cracks of a polycarbonate polymer. (June 2017)

• Record Type: Journal Article
• Authors: Hughes, J.M.; Lugo, M.; Bouvard, J.L.; McIntyre, T.; Horstemeyer, M.F.
• Is Part Of: International journal of fatigue. Volume 99:Part 1(2017)
• Page Start: 78
• Page End: 86
• Publication Date: 2017-06
• Subjects: Polycarbonate -- MultiStage Fatigue -- Uncertainty; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2016.12.012
• Languages: English
• ISSNs: 0142-1123

Abstract

Highlights: A metals crack growth model is considered for use with polymers. Crack incubating inclusions identified via fractographic analysis. Monte Carlo random sampling methods used to quantify model uncertainty. Identification of future improvements of the MultiStage Fatigue model for plastics. Abstract: The fatigue behavior of a polycarbonate (PC) thermoplastic material was experimentally investigated and modeled using a MultiStage Fatigue (MSF) model that evaluates fatigue crack incubation, Microstructurally Small Crack (MSC) growth, and Long Crack (LC) growth. A set of fully reversed strain controlled tests were conducted, and an analysis of the fracture surfaces was performed using Scanning Electron Microscopy (SEM) in order to quantify the structure-property relationships for the MSF model. Fractography of the microstructure revealed that incompletely melted PC pellets were present in the polymer material that nucleated the cracks along with crazes generated on the surface. Crack lengths and fatigue crack growth rates for the MSC regime were measured from striation observations on the fracture surfaces. Discontinuous crack growth (DCG) cycles between fatigue striations, for the current iteration of the model, are accounted for by the MSF crack incubation regime. Finally, the microstructure sensitive MSF model was implemented using the observed fatigue crack growth measurements. In addition, a Monte Carlo (MC) Simple Random Sampling (SRS) routine was implemented to quantify the model uncertainty for crack growth.