Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Konrad Lis"'
Publikováno v:
Advances in Sciences and Technology, Vol 17, Iss 5, Pp 350-359 (2023)
Skew rolling with three rolls is used for producing axisymmetric parts. In this method, the tools are spaced every 120° on the circumference of the workpiece. They are also set askew relative to the rolling axis. Cross sectional reduction is made ef
Externí odkaz:
https://doaj.org/article/9d89a7364c0941459c380d3dd995c48a
Publikováno v:
Advances in Sciences and Technology, Vol 17, Iss 2, Pp 173-180 (2023)
The article describes the problem of material fracture in metal forming processes. It describes and compares the values of damage functions obtained in classical tensile and torsion tests of two materials, i.e. CW008A copper and S355 steel under cold
Externí odkaz:
https://doaj.org/article/a5f41c8e23b247fea49ddc9f0aadce4f
Autor:
Konrad Lis
Publikováno v:
Materials, Vol 16, Iss 22, p 7136 (2023)
This paper presents results from experimental and numerical studies of the skew rolling process used to shape axisymmetric products made of C60-grade steel. An experimental study was carried out to investigate the effect of process parameters describ
Externí odkaz:
https://doaj.org/article/bf92badfcd2f4f07bde8ed473a591289
Publikováno v:
Advances in Sciences and Technology, Vol 14, Iss 1, Pp 145-153 (2020)
The article presents an innovative method of manufacturing hollow rail axles using three combined wedge rolls. The proposed solution was evaluated by using numerical simulation. Two cases of forming, differing in the wall thickness of the billet, wer
Externí odkaz:
https://doaj.org/article/84ad3fd849ef448bad87467324b07d8c
Publikováno v:
Advances in Sciences and Technology, Vol 12, Iss 2, Pp 77-82 (2018)
The paper addresses the problem of material fracture in cross rolling processes. A new test based on rotary compression for determining limit values of the damage function after the Cockroft-Latham criterion is proposed. A FEM analysis is performed t
Externí odkaz:
https://doaj.org/article/48db8a1c8af242419556411fdfd93c01
Publikováno v:
Materials, Vol 14, Iss 23, p 7126 (2021)
The article presents the results of model tests with which a comparative analysis of two methods of ball separation during the skew rolling process was carried out. A verification of the results obtained in the physical modelling process with the res
Externí odkaz:
https://doaj.org/article/79f53721222a4de4bc22d25225c52b8a
Autor:
Tomasz Kryjak, Konrad Lis
Publikováno v:
Computer Vision and Graphics ISBN: 9783031220241
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a03b7ed0bedf161d0a906c9729cd18f4
https://doi.org/10.1007/978-3-031-22025-8_8
https://doi.org/10.1007/978-3-031-22025-8_8
Publikováno v:
Journal of Signal Processing Systems. 94:659-674
In this paper, we present a hardware-software implementation of a deep neural network for object detection based on a point cloud obtained by a LiDAR sensor. The PointPillars network was used in the research, as it is a reasonable compromise between
Autor:
Marek Gorgoń, Maciej Aleksandrowicz, Krzysztof Błachut, Andrzej Brodzicki, Zbigniew Bubliński, Artur Cyba, Michał Daniłowicz, Adam Głowacz, Mirosław Jabłoński, Joanna Jaworek-Korjakowska, Aleksander Kostuch, Marcin Kowalczyk, Tomasz Kryjak, Dariusz Kucharski, Konrad Lis, Michał Machura, Zbigniew Mikrut, Piotr Pawlik, Michał Piekarski, Dominika Przewłocka-Rus, Joanna Stanisz, Hubert Szolc, Mateusz Wąsala, Anna Wójcicka, Piotr Wzorek
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d0550cfd0a1484f2f809675291833db
https://doi.org/10.7494/978-83-67427-00-5_1
https://doi.org/10.7494/978-83-67427-00-5_1
Autor:
Tomasz Kryjak, Konrad Lis
3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detectio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7a976d9914588e4336b22f59a53876d