Development of creep damage AI evaluation system for austenitic stainless steel

Abstract

An artificial intelligence evaluation system using neural network was developed for upgrading the creep damage assessment methodology through image analysis of EBSD(Electron BackScateer Diffraction pattern) maps. KAM(Kernel Average Misorientation) maps were obtained for creep damaged austenitic stainless steel SUS 304HTB and the stratified data were manipulated as the representatives of damage degrees. The system consists of an input layer, intermediate layers and an output layer. As the activation function, ReLU(Rectified Linear Unit) function is used for the intermediate layers and Softmax function is used for the output layer. The evaluation results of the proposed system were compared with the results of the conventional quantitative damage evaluation method. As a result, the estimated damage accuracy of the artificial intelligence evaluation system developed in this research was proved to be improved by about 3.3% compared with the estimated damage accuracy using the conventional evaluation method. Furthermore, the accuracy was improved by about 6.7% after the optimization of the neural network compared with the conventional evaluation method. Moreover, it was proved this system had sufficient robustness through the check tests for the case of extremely missing EBSD image. Thus machine learning utilizing neural network was expected to be a potential method for versatile data analysis applicable to various sort of metallographic study.

Development of damage evaluation system for heat resistant steel for creep and creep fatigue based on deep learning of grain shape and strain information by EBSD observation

“…Creep tests were conducted with the single lever type creep testing machine made by Toshin Kogyo Co., LTD at the temperature of 650 °C (923 K) and the stress of 130MPa in air. Creep tested specimens were interrupted and observed for the longitudinal cross sections at 0, 10, 20, 50, 80, and 100% of estimated rupture time (Kurashige & Fujiyama, 2019, Harada, 2015.…”

“…The authors have already applied the neural network to damage evaluation using KAM (Kernel Average Misorientation) parameters obtained by EBSD observation for interrupted creep test materials and interrupted creep-fatigue test materials (Kurashige & Fujiyama, 2019, Kurashige & Fujiyama, 2020. In these papers, the method using the neural network was superior to the conventional method using the screen average called master curve method and the parametric statistical methods in the accuracy of determining the damage rate.…”