Data science approaches to diagnostics of metal stress-strain state using semiconductor sensor suitable for system design

dc.contributor.author	Balitskii, O.
dc.contributor.author	Kolesnikow, W.
dc.contributor.author	Owsyannikow, A.
dc.contributor.author	Lizunow, S.
dc.contributor.author	Eliasz, J.
dc.date.accessioned	2019-05-12T21:03:21Z
dc.date.available	2019-05-12T21:03:21Z
dc.date.issued	2018
dc.identifier.uri	http://hdl.handle.net/123456789/3837
dc.description	Data science approaches to diagnostics of metal stress-strain state using semiconductor sensor suitable for system design / O. Balitskii and other // Badania Nieniszczące i Diagnostyka (Non-destructive testing and diagnostics). – 2018. – Vol. 4. – P. 38-41.	uk_UA
dc.description.abstract	Article describes the data science approaches to diagnostics of metal stressstrain state using semiconductor sensor suitable for system design. It has been described the elongation curves (on permanent loading 370-450 MPa) in time of St3 (kp, sp) specimens in initial state, after treatment in He and H2 with pressure 35 MPa and temperature 623 К during 10 hours as well as a curves of the average signal of semiconductor sensors that controls this process and spectral sensitivity of the semiconductor sensors of the visible range depending on the time of the exposure of the samples.	uk_UA
dc.language.iso	en	uk_UA
dc.subject	diagnostics	uk_UA
dc.subject	system design	uk_UA
dc.subject	semiconductor sensor	uk_UA
dc.title	Data science approaches to diagnostics of metal stress-strain state using semiconductor sensor suitable for system design	uk_UA
dc.type	Article	uk_UA