Abstract

Steels are the most used structural material in the world, and hydrogen content and localization within the microstructure play an important role in its properties, namely inducing some level of embrittlement. The characterization of the steels susceptibility to hydrogen embrittlement (HE) is a complex task requiring always a broad and multidisciplinary approach. The target of the present work is to introduce the artificial neural network (ANN) computing system to predict the hydrogen-induced mechanical properties degradation using the hydrogen thermal desorption spectroscopy (TDS) data of the studied steel. Hydrogen sensitivity parameter (HSP) calculated from the reduction of elongation to fracture caused by hydrogen was linked to the corresponding hydrogen thermal desorption spectra measured for austenitic, ferritic, and ferritic-martensitic steel grades. Correlation between the TDS input data and HSP output data was studied using two ANN models. A correlation of 98% was obtained between the experimentally measured HSP values and HSP values predicted using the developed densely connected layers ANN model. The performance of the developed ANN models is good even for never-before-seen steels. The ANN-coupled system based on the TDS is a powerful tool in steels characterization especially in the analysis of the steels susceptibility to HE.

中文翻译：

---

利用人工神经网络研究钢的氢脆敏感性和氢热脱附光谱之间的相关性

摘要

钢材是世界上使用最广泛的结构材料，氢含量和微观结构中的局部化在其性能中起着重要作用，即引起一定程度的脆化。表征钢对氢脆性（HE）的敏感性是一项复杂的任务，需要始终采用广泛和多学科的方法。当前工作的目标是引入人工神经网络（ANN）计算系统，以使用研究钢的氢热脱附光谱（TDS）数据预测氢诱导的机械性能退化。由氢引起的断裂伸长率降低所计算出的氢敏感性参数（HSP）与测量到的奥氏体，铁素体，和铁素体-马氏体钢牌号。使用两个ANN模型研究了TDS输入数据和HSP输出数据之间的相关性。在实验测量的HSP值和使用发达的紧密连接层ANN模型预测的HSP值之间获得了98％的相关性。即使对于从未见过的钢材，已开发的ANN模型的性能也很好。基于TDS的ANN耦合系统是进行钢特性分析的强大工具，尤其是在分析钢对HE的敏感性方面。即使对于从未见过的钢材，已开发的ANN模型的性能也很好。基于TDS的ANN耦合系统是进行钢特性分析的强大工具，尤其是在分析钢对HE的敏感性方面。即使对于从未见过的钢材，已开发的ANN模型的性能也很好。基于TDS的ANN耦合系统是进行钢特性分析的强大工具，特别是在分析钢对HE的敏感性方面。