Bayesian synthesis for simulation-based generation of probability of detection (PoD) curves.

This paper examines the feasibility of using Bayesian synthesis to reduce the number of experimental cases and trials required for generation of probability of detection (PoD) curves. A Bayesian framework is developed for the data-level combination of experimental and simulated datasets, in the context of the inspection of back-wall breaking notches in metallic samples by bulk ultrasonic shear waves. PoD curves generated using the proposed approach, where results from a reduced number of experimental defect cases and trials are used in combination with simulated datasets, are shown to compare well with those from the conventional approach using a large number of experiments. Finally, the framework is also shown to be versatile for generating PoD curves for complex defects (illustrated through the example of an inclined notch) using simulations for canonical defects (vertical notches).