Abstract
The influence of temperature on mechanical characteristics during stretching of elastic material is investigated, functional dependence is created by means of neural networks. Perceptrons with a varying number of neurons were used. High-strength heat-resistant complex filaments were the samples in experiments. The tests were carried out at 5 different temperature levels. On the basis of the obtained data, the functional dependence of each of the parameters on the temperature of the material was revealed. For polyester thread, methods for processing data from an accelerated experiment are given, which make it possible to significantly shorten the time of its carrying out. The methods considered are universal and can be applied to other elastic materials.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bolgov, I., Kaverzneva, T., Kolesova, S., Lazovskaya, T., Stolyarov, O., Tarkhov, D.: Neural network model of rupture conditions for elastic material sample based on measurements at static loading under different strain rates. In: Journal of Physics: Conference Series, 2016 Joint IMEKO TC1-TC7-TC13 Symposium: Metrology across the Sciences: Wishful Thinking?, Berkeley, USA, 3–5 August 2016, vol. 772, p. 012032 (2016). https://doi.org/10.1088/1742-6596/772/1/012032
Vasilyev, A., et al.: Multilayer neural network models based on experimental data for processes of sample deformation and destruction. In: CEUR Workshop Proceedings, Selected Papers of the First International Scientific Conference Convergent Cognitive Information Technologies (Convergent 2016), Moscow, Russia, 25–26 November 2016, vol. 1763, pp. 6–14 (2016). http://ceur-ws.org/Vol-1763/paper01.pdf. (in Russian)
Filkin, V., et al.: Neural network modeling of conditions of destruction of wood plank based on measurements. In: Journal of Physics: Conference Series, 2016 Joint IMEKO TC1-TC7-TC13 Symposium: Metrology across the Sciences: Wishful Thinking?, Berkeley, USA, 3–5 August 2016, vol. 772, p. 012041 (2016). https://doi.org/10.1088/1742-6596/772/1/012041
Aranda-Iglesias, D., Vadillo, G., RodrÃguez-MartÃnez, J.A., Volokh, K.Y.: Modeling deformation and failure of elastomers at high strain rates. Mech. Mater. 104, 85–92 (2017). https://doi.org/10.1016/j.mechmat.2016.10.004
Zéhil, G.-P., Gavin, H.P.: Unified constitutive modeling of rubber-like materials under diverse loading conditions. Int. J. Eng. Sci. 62, 90–105 (2013). https://doi.org/10.1016/j.ijengsci.2012.09.002
Kaverzneva, T.T., Smirnova, O.V.: Wear-out effect of construction equipment and hand tools on workers’ labor conditions. Saf. Technosphere 2(3), 14–18 (2013). https://doi.org/10.12737/446. (in Russian)
Efremov, S.V., Kaverzneva, T.T., Tarkhov, D.A.: Neural Network Modeling in Labor Protection. Publishing House of SPbSPU, Saint-Petersburg (2014). (in Russian)
Acknowledgements
This paper is based on research carried out with the financial support of the grant of the Russian Scientific Foundation (project â„– 18-19-00474).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Filchuk, E. et al. (2021). Neural Network Installation of the Functional Dependence of Mechanical Behavior in the Expansion of Elastic Material from Temperature. In: Sukhomlin, V., Zubareva, E. (eds) Modern Information Technology and IT Education. SITITO 2017. Communications in Computer and Information Science, vol 1204. Springer, Cham. https://doi.org/10.1007/978-3-030-78273-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-78273-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78272-6
Online ISBN: 978-3-030-78273-3
eBook Packages: Computer ScienceComputer Science (R0)