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Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Development of autonomous experimental system to analyse yield surfaces distortion due to multiaxial ratcheting
 Svárovský, Jiří ; Parma, Slavomír ; Štefan, Jan ; Ciocanel, C. ; Feigenbaum, H. P. ; Marek, René ; Klepač, Vilém ; Plešek, Jiří
Multiaxial ratcheting is a failure mode of structures characterized by the accumulation of plastic strain due to cyclic loading. Despite several models having been developed to predict multiaxial ratcheting, they often fail when validated with experimental data collected under a wide array of loading conditions. In this study, an experimental setup was developed and an autonomous testing procedure was used to experimentally analyze the evolution of the yield surface shape due to cyclic biaxial loading. Thin-walled tubular test specimens were made of 304L steel with a diameter of 40mm and underwent axial-torsional testing using the Instron 8852 system. The total axial strain was increased from 0 to 1% while the total shear strain underwent 5 cycles with the strain amplitude of 0.5% and the mean strain of 0.5%. Three yield surfaces were measured after the straining sequence was completed. Results showed strong directional distortional hardening and good agreement between the flow vectors and the normals to the yield surface, lending support to the associative flow rule.
Úplný záznam
Deep learning methods for the acoustic emission methods to evaluate an onset of plastic straining
 Parma, Slavomír ; Kovanda, Martin ; Chlada, Milan ; Štefan, Jan ; Kober, Jan ; Feigenbaum, H. P. ; Plešek, Jiří
Development of phenomenological plasticity models, hardening rules, and plasticity theories relies on experimental data of plastic straining. The experimental data are usually measured as the stress–strain response of the material being loaded and do not provide any clues or information about the local response of\nmaterial. In this paper, we analyze the plastic deformation of the material using the acoustic emission method and current state-of-the-art neural network models such as the InceptionTime architecture.
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