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.
View Article and Find Full Text PDFThermal 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.
View Article and Find Full Text PDFA 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.
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