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.

Keywords

• creep damage
• sus304htb
• ebsd
• misorientation
• artificial intelligence
• machine learning
• supervised learning
• neural network
• deep learning