PNNL

Abstract

Ultrasonic nondestructive evaluation (NDE) and inspection of cast austenitic stainless steel (CASS) components used in the nuclear power industry is neither as effective nor reliable as is needed due to detrimental effects upon the interrogating ultrasonic beam and interference from ultrasonic backscatter. The root cause is the coarse-grain microstructure inherent to this class of materials. Some ultrasonic techniques perform better for particular microstructural classifications and this has led to the hypothesis that an ultrasonic inspection can be optimized for a particular microstructural class, if a technique exists to reliably classify the microstructure for feedback to the inspection. This document summarizes scoping experiments of in-situ ultrasonic methods for classification and/or characterization of the material microstructures in CASS components from the outside surface of a pipe. The focus of this study was to evaluate ultrasonic methods and provide an interim report that documents results and technical progress. An initial set of experiments were performed to test the hypothesis that in-service characterization of cast austenitic stainless steel (CASS) is feasible, and that, if reliably performed, such data would provide real-time feedback to optimize in-service inspections in the field. With this objective in mind, measurements for the experiment were restricted to techniques that should be robust if carried forward to eventual field implementation. Two parameters were investigated for their ability to discriminate between different microstructures in CASS components. The first parameter was a time-of-flight ratio of a normal incidence shear wave to that of a normal incidence longitudinal wave (TOFRSL). The ratio removed dependency on component thickness which may not be accurately reported in the field. The second parameter was longitudinal wave attenuation. The selected CASS specimens provided five equiaxed-grain material samples and five columnar-grain material samples for a two-class discrimination problem. Qualitative TOFRSL estimates and a threshold algorithm classified all 10 material samples correctly and indicated a reliable and robust technique. Qualitative longitudinal wave attenuation estimates and a threshold algorithm also classified all 10 materials samples correctly; however, the technique was not as robust as TOFRSL. The experiments provided promising results and demonstrated that good potential exists for future development of techniques to implement real-time classification of CASS material. However, the reported measurements need to be substantiated with measurements on additional specimens.