A Machine Learning Approach to Predict the Materials' Susceptibility to Hydrogen Embrittlement

Abstract

Hydrogen is widely considered a promising energy carrier capable of mitigating human environmental impact. Nevertheless, safety aspects represent one of the major bottlenecks for the widespread utilization of hydrogen technologies. Industrial equipment operating in hydrogen environments is prone to hydrogen-induced damages, which may manifest through a reduction of mechanical properties, fracture toughness, and fatigue performance. They may cause component failures at stress levels significantly below the design level, therefore determining loss of containment. The occurrence of hydrogen embrittlement (HE) relies on the synergy of several factors, such as hydrogen concentration, operating conditions, level of internal and applied stress, microstructure and chemical composition of the material. However, the interlinked dependence of these factors makes a direct and clear evaluation challenging, subsequently creating serious difficulties in planning inspection and maintenance activities. In this study, a comprehensive review of the experimental data of tensile tests carried out in hydrogen was performed and analyzed through an advanced machine learning approach. This study can provide critical insights into the susceptibility to hydrogen embrittlement for several materials operating under different environmental conditions. In particular, the Embrittlement Index was estimated and used as determining parameter to predict the likelihood of component failures. The model demonstrated accurate and reliable predicting capabilities. The outcome of this study can increase the understanding of hydrogen-induced material damages and facilitate decision-making processes in planning the inspection and maintenance of hydrogen technologies.