Experimental and numerical investigation of mixed mode fatigue crack growth models in aluminum 6061-T6
Source abstract
Abstract
Abstract Damage tolerance philosophy and safe life design principles are widely used for critical structural components, and in such cases, fatigue life prediction using the numerical techniques is essential. In the present work, mixed mode fatigue crack growth experiments are performed using the compact tension shear specimens made of Al 6061-T6 alloy for mode mixity angles of 30°, 45°, and 60°. These experimental studies are supported by numerical and fractographic studies on the selected specimens. A three-parameter double exponential model for fitting the fatigue crack growth curves (crack length vs. the number of fatigue life cycles) is also proposed for both the mode I and mixed mode (I/II) loading conditions. Numerical prediction of mixed mode (I/II) fatigue life is carried out using the Paris’ law in combination with various Δ K eq models. The results of the present investigation show that highly satisfactory best fits to the scattered experimental data can be obtained with the help of the proposed double exponential model. The predicted life is compared with the experimental fatigue life using various measures of error. Based on the overall performance of the models during the entire crack growth regime, the results of the present investigation clearly show that Irwin’s model and one of the Tanaka’s model are consistently found to predict the mixed mode fatigue life close to the experimental data. Whereas, Richard’s and Yan’s models, again based on the overall performance, are found to be conservative models consistently for the prediction of the mixed mode fatigue life. These conclusions are verified with the published results.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Fatigue and fracture mechanics · Numerical methods in engineering · Probabilistic and Robust Engineering Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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