Classification of ultrasonic signals of thermally aged cast austenitic stainless steel (CASS) using machine learning (ML) models

Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. The main drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operating environment. Even though ultrasonic non-destructive testing has been conducted in major nuclear components and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonic signals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonic technique (UT) and machine learning (ML) models. Several different ML models, specifically the K nearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models, were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identified that the ML models can predict the category of ultrasonic signals effectively according to the aging condition. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Cast austenitic stainless steels (CASSs) are commonly used in the nuclear industry, but their fracture toughness may decrease over time due to exposure to the operating environment. Despite challenges in crack detection caused by the scattering and attenuation of ultrasonic waves in CASS materials, machine learning models like KNN, SVM, and MLP have shown promising results in classifying ultrasonic signals based on aging condition.