Fatigue Time-to-Failure Prediction Methodology for Glass (Fused Quartz) Material under Cyclic Loading

Paper #:
  • 2016-01-0388

  • 2016-04-05
  • 10.4271/2016-01-0388
Pandey, A., Singhal, M., Kovacich, J., and Rau, C., "Fatigue Time-to-Failure Prediction Methodology for Glass (Fused Quartz) Material under Cyclic Loading," SAE Technical Paper 2016-01-0388, 2016, doi:10.4271/2016-01-0388.
In amorphous solids such as fused quartz, the failure mechanism under cyclic loading is very different when compared to metals where this failure is attributable to dislocation movement and eventual slip band activity. Standard mechanical fatigue prediction methodologies, S-N or ε-N based, which have been historically developed for metals are rendered inapplicable for this class of material. The fatigue strength of Fused Silica or Fused Quartz (SiO2) material is known to be highly dependent on the stressed area and the surface finish. Stable crack growth in Region II of the V-K curve (Crack growth rate vs Stress intensity factor) is dependent on the competing and transitional effects of temperature and humidity, along that specific section of the stress intensity factor abscissa. Fused glass (under harsh environment conditions) finds usage in Automotive, Marine and Aerospace applications, where stress and load (both static and cyclic) can be severe.In the present work we have developed a six-step systematic approach for the probabilistic design of glass under dynamic loads. Specifically, ultraviolet irradiation reactor tubes, made of fused quartz and used to treat water, are used to demonstrate application of our methodology, and illustrate the challenges involved in the design of brittle material versus fatigue failure. Our methodology uses (1) a Weibull 2-parameter model, including the area scaling principle, (2) the shape parameter, which is essentially independent of the stressed area as well as the surface finish; and (3) the characteristic strength (based on 1cm2 uniformly stressed area).Significant variability is observed in the published material data. Monte-Carlo simulations were performed to account for experimental variability in the time-to-failure predictions.
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