Prof. Ayhan Ince has more than 10 years of experience in the defense industry in research and development. He was an Assistant Professor at Purdue University from 2014-2017 and joined Concordia University summer of 2017 as an Associate Professor. His research primarily addresses fatigue and fracture mechanics, computational damage modeling, short crack behavior, spectrum fatigue, machine learning and peridynamics modeling.
AbstractMany fatigue crack growth prediction models have been developed to consider both short crack and long crack behaviors by accounting for the stress intensity range-based crack driving forces or crack closure-based concepts. Despite some successes, all of those fatigue crack growth models lacked mechanically comprehensive approach to fully address the complex behavior of short cracks, thus it led to very limited applications. Based on the recent systematic study performed in the author’s group, a new generalized two-parameter driving force model has been developed to account for crack growth driving forces and corresponding crack growth thresholds to predict both short crack and long crack propagation behaviors. Fatigue crack growth rates predicted by the proposed model are compared with fatigue crack growth and life data set of short and long cracks for various stress levels at R-ratios of 0.1 and 0.5 for the titanium alloy, Ti-6Al-4V. The model predicted results are also compared to short and long crack propagation data set of the aluminum alloy, 2024-T3 for various stress levels at R-ratios of 0.0 and 0.5. The predicted results show good agreement with experimental crack growth data set for these alloys. Based on the comparison of crack growth predictions and fatigue life results with experimental data set, the proposed model demonstrates good predictive capability to account for multiple critical driving force parameters to accurately simulate crack growth behaviors in both short and long crack regimes.
|Room 6||Thursday 30th November||17:00-17:45||Ayhan Ince|
8 - Generalized modelling approach to predict short and long fatigue crack propagation behaviors