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Academic literature on the topic 'Jump-like deformation'
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Journal articles on the topic "Jump-like deformation"
Kawabe, Takahiro. "Perceiving Animacy From Deformation and Translation." i-Perception 8, no. 3 (May 17, 2017): 204166951770776. http://dx.doi.org/10.1177/2041669517707767.
Full textMüller, Toni, Jens-Uwe Sommer, and Michael Lang. "Tendomers – force sensitive bis-rotaxanes with jump-like deformation behavior." Soft Matter 15, no. 18 (2019): 3671–79. http://dx.doi.org/10.1039/c9sm00292h.
Full textYasnii, P. V., Yu I. Pyndus, V. B. Hlad’o, and I. V. Shul’han. "Computer modeling of the jump-like deformation of AMg6 alloy." Materials Science 44, no. 1 (January 2008): 43–48. http://dx.doi.org/10.1007/s11003-008-9041-y.
Full textFedak, Serhii, Oleg Yasnii, Iryna Didych, and Nadiya Kryva. "Characteristics of the deformation diagram of AMg6 alloy." Scientific journal of the Ternopil national technical university 110, no. 2 (2023): 33–39. http://dx.doi.org/10.33108/visnyk_tntu2023.02.033.
Full textLebedev, V. P., V. S. Krylovskiĭ, S. V. Lebedev, and S. V. Savich. "Low-amplitude jump-like deformation of Pb–In alloys in the superconducting state." Low Temperature Physics 34, no. 3 (March 2008): 234–40. http://dx.doi.org/10.1063/1.2889412.
Full textYasniy, Oleh, Iryna Didych, Sergiy Fedak, and Yuri Lapusta. "Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods." Procedia Structural Integrity 28 (2020): 1392–98. http://dx.doi.org/10.1016/j.prostr.2020.10.110.
Full textDolgin, A. M., and V. Z. Bengus. "Kinetics of high-velocity processes of low temperature jump-like deformation of niobium." physica status solidi (a) 94, no. 2 (April 16, 1986): 529–35. http://dx.doi.org/10.1002/pssa.2210940212.
Full textKirichenko, G. I., V. D. Natsik, V. V. Pustovalov, V. P. Soldatov, and S. E. Shumilin. "Jump-like deformation of single crystals of Sn–Cd alloys at temperatures ≲1 K." Low Temperature Physics 23, no. 9 (September 1997): 758–64. http://dx.doi.org/10.1063/1.593374.
Full textPustovalov, V. V. "Influence of superconducting transition on low temperature jump-like deformation of metals and alloys." Materials Science and Engineering: A 234-236 (August 1997): 157–60. http://dx.doi.org/10.1016/s0921-5093(97)00151-2.
Full textMizutani, Yasushi, Susumu Tamai, Toshifumi Nakamura, Takehiko Takita, and Shohei Omokawa. "Magnetic Resonance Imaging Evaluation of Acute Plastic Deformation of a Pediatric Radius." Journal of Hand Surgery (Asian-Pacific Volume) 26, no. 02 (January 11, 2021): 280–83. http://dx.doi.org/10.1142/s2424835521720085.
Full textDissertations / Theses on the topic "Jump-like deformation"
Didych, Iryna. "Estimation of structural integrity and lifetime of important structural elements." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2021. http://www.theses.fr/2021UCFAC116.
Full textThis work has been performed under co-tutelle supervision between Ternopil IvanPuluj National Technical University in Ternopil (Ukraine) and UniversityClermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal in Clermont-Ferrand (France).This thesis solves the scientific task of responsible structural elements strength andlifetime evaluation. The aim of the thesis is to evaluate the strength and residuallifetime of structural elements by machine learning methods.Most parts of machines and structural elements while being in service are under theinfluence of loads of various nature. Such forces are applied either directly to theelement or transmitted through neighbor elements connected to it. For the normaloperation of the responsible structures parts, each element must have certain sizeand shape that will withstand the loads acting on it. In particular, it must haveappropriate strength properties, not deform significantly under the action ofstresses, be rigid, and preserve its original shape.The calculated residual lifetime of machines and structures can be predicted usingfatigue crack growth (FCG) diagrams. Often, the experimental data have a certainspread, which should be taken into account in their analysis. The experimentalmethod often takes a lot of time and human resources. Therefore, it is advisable tolearn how to calculate the residual lifetime using machine learning methods,particularly, neural networks, boosted trees, random forests, support-vectormachines and the method of k–nearest neighbors