Evgenii Malitckii

The influence of hydrogen on the mechanical performance of a hot-rolled martensitic steel was studied by means of constant extension rate test (CERT) and constant load test (CLT) followed with thermal desorption spectroscopy measurements. The steel shows a reduction in tensile strength up to 25% of ultimate tensile strength (UTS) at critical hydrogen concentrations determined to be about 1.1 wt.ppm and 50% of UTS at hydrogen concentrations of 2 wt.ppm. No further strength degradation was observed up to hydrogen concentrations of 4.8 wt.ppm. It was observed that the interplay between local hydrogen concentrations and local stress states, accompanied with the presence of total average hydrogen reducing the general plasticity of the specimen are responsible for the observed strength degradation of the steel at the critical concentrations of hydrogen. Under CLT, the steel does not show sensitivity to hydrogen at applied loads below 50% of UTS under continuous electrochemical hydrogen charging up to 85 h. Hydrogen enhanced creep rates during constant load increased linearly with increasing hydrogen concentration in the steel.

A novel approach has been developed for quantitative evaluation of the susceptibility of steels and alloys to hydrogen embrittlement. The approach uses a combination of hydrogen thermal desorption spectroscopy (TDS) analysis with recent advances in machine learning technology to develop a regression artificial neural network (ANN) model predicting hydrogen-induced degradation of mechanical properties of steels. We describe the thermal desorption data processing, artificial neural network architecture development, and the learning process beneficial for the accuracy of the developed artificial neural network model. A data augmentation procedure was proposed to increase the diversity of the input data and improve the generalization of the model. The study of the relationship between thermal desorption spectroscopy data and the mechanical properties of steel evidences a strong correlation of their corresponding parameters. A prototype software application based on the developed model is introduced and is openly available. The developed prototype based on TDS analysis coupled with ANN is shown to be a valuable engineering tool for steel characterization and quantitative prediction of the degradation of steel properties caused by hydrogen.

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.

Thermal desorption spectroscopy (TDS) is a powerful method for the measurement of hydrogen concentration in metallic materials. However, hydrogen loss from metallic samples during the preparation of the measurement poses a challenge to the accuracy of the results, especially in materials with high diffusivity of hydrogen, like ferritic and ferritic-martensitic steels. In the present paper, the effect of specimen cooling during the experimental procedure, as a tentative to reduce the loss of hydrogen during air-lock vacuum pumping for one high-strength steel of 1400 MPa, is evaluated. The results show, at room temperature, the presence of a continuous outward hydrogen flux accompanied with the redistribution of hydrogen within the measured steel during its exposure to the air-lock vacuum chamber under continuous pumping. Cooling of the steel samples to 213 K during pumping in the air-lock vacuum chamber before TDS measurement results in an increase in the measured total hydrogen concentration at about 14%. A significant reduction in hydrogen loss and redistribution within the steel sample improves the accuracy of hydrogen concentration measurement and trapping analysis in ferritic and martensitic steels.

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the growth of microstructurally small fatigue cracks. The proposed strain field analysis methodology is based on the use of a unique pattering technique with a characteristic speckle size of approximately 10 µm. The developed methodology is applied to study the small fatigue crack behavior in body centered cubic (bcc) Fe-Cr ferritic stainless steel with a relatively large grain size allowing a high spatial measurement accuracy at the sub-grain level. This methodology allows the measurement of small fatigue crack growth retardation events and associated intermittent shear strain localization zones ahead of the crack tip. In addition, this can be correlated with the grain orientation and size. Thus, the developed methodology can provide a deeper fundamental understanding of the small fatigue crack growth behavior, required for the development of robust theoretical models for the small fatigue crack propagation in polycrystalline materials.