In this paper, we propose a two-step approach to addressing the question of how reliable the practice of applying laboratory data on fracture toughness to full-scale structures and components is. In the first step, we construct a two-level eight-factor 16-run-plus-a-center-point fractional factorial orthogonal design of experiments and conduct an analysis using literature fracture toughness and parameter data, where the eight factors are: (1) the Young’s modulus, E, (2) a material property constant, α, as defined in a Ramberg-Osgood stress-strain model, (3) the work hardening exponent, n, of the same model, (4) the yield stress, σy, (5) the critical local fracture stress, σf, (6) a chemical composition parameter in the form of the ratio of manganese to carbon content, Mn/C, (7) the crack depth/width ratio, a/W, and (8) the critical microstructural distance, rc, from the crack tip. Based on the 17-run data and the design of experiments analysis, we first obtain a ranking of the relative importance of those eight factors and then select two most important ones, to be named “key parameters”, to perform a multi-linear least square fit of the fracture toughness data as a function of those two key parameters. This simplification allows us to calculate, for the number of tests equal to N (= 17), the best estimate of the fracture toughness with 95% confidence prediction intervals. In the second step, we apply the statistical concept of a tolerance interval for a fixed sample size N and three coverages, 90%, 95%, and 99%, to a conversion of the results of the first step (the prediction intervals) to a set of tolerance intervals for the fracture toughness of a full-scale structure. Significance and limitations of this novel approach to answering the question of reliability from laboratory data to full scale structures are discussed at the end of this presentation.
Dynamic aspects during full scale rotating bending axle tests using new generation of facilities with increased load frequency