5.1 Fracture toughness is expressed in terms of an elastic-plastic stress intensity factor, KJc, that is derived from the J-integral calculated at fracture.
5.2 Ferritic steels are microscopically inhomogeneous with respect to the orientation of individual grains. Also, grain boundaries have properties distinct from those of the grains. Both contain carbides or nonmetallic inclusions that can act as nucleation sites for cleavage microcracks. The random location of such nucleation sites with respect to the position of the crack front manifests itself as variability of the associated fracture toughness (16). This results in a distribution of fracture toughness values that is amenable to characterization using the statistical methods in this test method.
5.3 The statistical methods in this test method presume that the test materials are macroscopically homogeneous such that both the tensile and toughness properties are uniform. The fracture toughness evaluation of nonuniform materials is not amenable to the statistical analysis methods employed in the main body of this test method. For example, multipass weldments can create heat-affected and brittle zones with localized properties that are quite different from either the bulk material or weld. Thick section steel also often exhibits some variation in properties near the surfaces. An appendix to analyze the cleavage toughness properties of nonuniform or inhomogeneous materials is currently being prepared. In the interim, users are referred to (6-8) for procedures to analyze inhomogeneous materials. Metallographic analysis can be used to identify possible nonuniform regions in a material. These regions can then be evaluated through mechanical testing such as hardness, microhardness, and tensile testing to compare with the bulk material. It is also advisable to measure the toughness properties of these nonuniform regions distinctly from the bulk material.
5.4 Distributions of KJc data from replicate tests can be used to predict distributions of KJc for different specimen sizes. Theoretical reasoning (12), confirmed by experimental data, suggests that a fixed Weibull slope of 4 applies to all data distributions and, as a consequence, standard deviation on data scatter can be calculated. Data distribution and specimen size effects are characterized using a Weibull function that is coupled with weakest-link statistics (17). An upper limit on constraint loss and a lower limit on test temperature are defined between which weakest-link statistics can be used.
5.5 The experimental results can be used to define a master curve that describes......
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