Prognosis methods of stress corrosion cracking under harsh environmental conditions

Abstract

Stress corrosion cracking (SCC) under harsh environmental conditions still poses a significant challenge, despite extensive research efforts. The intricate interplay among mechanical, chemical, and electrochemical factors hinders the accurate prognosis of material degradation and remaining service life. Furthermore, the demand for real-time monitoring and early detection of SCC defects adds further complexity to the prognostication process. Therefore, there is an urgent need for comprehensive review papers that consolidate current knowledge and advancements in prognosis methods. Such reviews would facilitate a better understanding and resolution of the challenges associated with SCC under harsh environmental conditions. This work aims to provide a comprehensive overview of various prognosis methods utilized for the assessment and prediction of SCC in such environments. The paper will delve into the following sections: exacerbating harsh environmental conditions, non-destructive testing (NDT) techniques, electrochemical techniques, numerical modeling, and machine learning. This review is inclined to serve as a valuable resource for researchers and practitioners working in the field, facilitating the development of effective strategies to mitigate SCC and ensure the integrity and reliability of materials operating in challenging environments. Despite considerable research, stress corrosion cracking in harsh environments remains a critical issue, complicated by the interplay of mechanical, chemical, and electrochemical factors. This review aims to consolidate current prognosis methods, including non-destructive testing, electrochemical techniques, numerical modeling, and machine learning. Key findings indicate that while traditional methods offer limited reliability, emerging computational approaches show promise for real-time, accurate predictions. The paper also briefly discusses notable SCC failure cases to underscore the urgency for improved prognosis techniques. This work aspires to fill knowledge gaps and serve as a resource for developing effective SCC mitigation strategies, thereby ensuring material integrity in challenging operational conditions.

Keywords

• SCC
• Artificial intelligence
• Machine learning
• Electrochemical testing
• Slow strain rate
• Ultrasonic testing