Dual phase (DP) steel was intercritically annealed at different temperatures from fully martensitic state to achieve martensite plus ferrite, microstructures with martensite contents in the range of 32 to 76%. Fatigue crack growth (FCG) and fracture toughness tests were carried out as per ASTM standards E 647 and E 399, respectively to evaluate the potential of DP steels. The crack growth rates (da/dN) at different stress intensity ranges (∆ ∆K) were determined to obtain the threshold value of stress intensity range (∆ ∆K th). Crack path morphology was studied to determine the influence of microstructure on crack growth characteristics. After the examination of crack tortuosity, the compact tension (CT) specimens were pulled in static mode to determine fracture toughness values. FCG rates decreased and threshold values increased with increase in vol.% martensite in the DP steel. This is attributed to the lower carbon content in the martensite formed at higher intercritical annealing (ICA) temperatures, causing retardation of crack growth rate by crack tip blunting and/or deflection. Roughness induced crack closure was also found to contribute to the improved crack growth resistance at higher levels of martensite content. Scanning electron fractography of DP steel in the near threshold region revealed transgranular cleavage fracture with secondary cracking. Results indicate the possibility that the DP steels may be treated to obtain an excellent combination of strength and fatigue properties. Keywords. Dual phase (DP) steel; fatigue crack growth; fracture toughness; stress intensity range; intercritical annealing; crack closure.
An artificial neural network (ANN)‐based model was developed to analyse high‐cycle fatigue crack growth rates (da/dN ) as a function of stress intensity ranges (ΔK ) for dual phase (DP) steel. The training data consisted of da/dN at ΔK ranges between 5 and 16 MPa √ for DP steel with martensite contents in the range 32 to 76%. The ANN back‐propagation model with Gaussian activation function exhibited excellent agreement with the experimental results. The fatigue crack growth rate predictions were made to demonstrate its practical significance in a given real‐life situation. Because of the wide range of data points used during training of the model, it will provide a useful predictor for fatigue crack growth in DP steels.
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