Conditional generative adversarial network-based predictive method for crack initiation in a dual-phase austenite stainless weld
Source abstract
Abstract
A novel integration approach of a conditional generative adversarial network (cGAN) with an improved electro-chemo-mechanical (E−C−M) model was developed to calculate the localised electrochemistry and evaluate the stress corrosion cracking (SCC) crack initiation properties in high-temperature water. The results demonstrate the superior accuracy, computational efficiency and robust generalisation capabilities of the hybrid method. High-temperature electrochemical tests were conducted to validate and optimize the E−C−M model for calculating the stress induced corrosion behavior of austenite stainless steel (ASS) in high-temperature water. The SCC initiation test results corroborate the predicted corrosion current distribution on ASS with a dual-phase structure.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Welding Techniques and Residual Stresses · Microstructure and Mechanical Properties of Steels
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (73)
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