Improved test method and analytical modelling for fatigue crack growth in coarse-grain titanium alloy with rough fatigue surfaces

Fatigue crack growth rate properties are typically determined by experimental methods in accordance with ASTM Standard E647. These traditional methods use standard notched specimens that are precracked under cyclic tensile loads before the main test. The data that are produced using this approach have been demonstrated elsewhere to be potentially adversely affected by the test method, particularly in the threshold region where load reduction (LR) methods are also required. Coarse‐grained materials that exhibit rough and tortuous fatigue surfaces have been observed to be strongly affected by the tensile precracking and LR, in part because the anomalies caused by crack closure and roughness‐induced closure become more important. The focus of the work reported in this paper was to further develop methods to determine more accurate fatigue crack growth rate properties from threshold through to fracture for coarse‐grained, β‐annealed, titanium alloy Ti‐6Al‐4V extra low interstitial thick plate material. A particular emphasis was put upon the threshold and near threshold region, which is of strong importance in the overall fatigue life of components. New approaches that differ from the ASTM Standard included compression precracking, LR starting from a lower load level and continuing the test beyond rates where crack growth would otherwise be considered below threshold. For the threshold regime, two LR methods were also investigated: the ASTM method and a method where the load is reduced with crack growth such that the crack mouth opening displacement is held constant, in an attempt to avoid remote closure. Constant amplitude fatigue crack growth rate data were produced from threshold to fracture for the titanium alloy at a variety of stress ratios. Spike overload tests were also conducted These data were then used to develop an improved analytical model to predict crack growth under spectrum loading and the predictions were found to correlate well with test results