Use of Large Databases to Identify Trends in the Behavior of Ferritic Steels

In this paper we explore the crucial role played by the use of large databases in the identification, development, and refinement of models that describe the toughness behavior of ferritic steels. Specifically, we seek to emphasize and illustrate the point that when physical models are calibrated using large databases this process can reveal trends not previously seen, or foreseen. In support of this idea two examples are cited. First, the evidence for a CVE master curve in fracture mode transition is reviewed, as a counterpoint to the commonly held belief that each Charpy tanh transition curve is unique, with little commonality even within specific alloys, let alone across all ferritic steels. Second, new evidence is presented that the degree of prior hardening experienced by a ferritic steel has a systematic effect on the scatter exhibited by K_Jc data. This evidence suggests that the K_Jc Master Curve model, in which the scatter of K_Jc follows a Weibull distribution having a K_min = 20 MPa√m and a slope (scatter magnitude) of 4, requires refinement, especially for the higher T_o values characteristic of steels that have been hardened by, for example, neutron irradiation damage.