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Grzegorz Królczyk
Researcher at Opole University of Technology
Publications - 288
Citations - 7824
Grzegorz Królczyk is an academic researcher from Opole University of Technology. The author has contributed to research in topics: Machining & Computer science. The author has an hindex of 41, co-authored 198 publications receiving 4659 citations. Previous affiliations of Grzegorz Królczyk include University of Ljubljana & Opole University.
Papers
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Journal ArticleDOI
Tribological and thermal behavior with wear identification in contact interaction of the Ti6Al4V-sintered carbide with AlTiN coatings pair
Marta Bogdan-Chudy,Piotr Niesłony,Munish Kumar Gupta,Szymon Wojciechowski,Radoslaw W. Maruda,Józef Gawlik,Grzegorz Królczyk +6 more
TL;DR: In this article, the effect of sliding distance on friction coefficient value is very low and micrographs of wear surface reveal that the sliding distance plays a major role in generation of abrasion marks with adhesive joints as well cold weld junctions.
Journal ArticleDOI
Studies on Geometrical Features of Tool Wear and Other Important Machining Characteristics in Sustainable Turning of Aluminium Alloys
Munish Kumar Gupta,Piotr Niesłony,Murat Sarikaya,Mehmet Erdi Korkmaz,Mustafa Kuntoğlu,Grzegorz Królczyk +5 more
TL;DR: In this paper , a new approach of measurement was adopted to measure the critical geometrical aspects of tool wear, surface roughness, power consumption and microhardness while machining AA2024-T351 alloy under dry, minimum quantity lubrication (MQL), liquid nitrogen (LN 2 ) and carbon dioxide (CO 2 ) cooling conditions.
Journal ArticleDOI
Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation
Xuejun Zhao,Rafał Stanisławski,Paolo Gardoni,Maciej Sułowicz,Adam Glowacz,Grzegorz Królczyk,Zhixiong Li +6 more
TL;DR: This paper proposes label consistent contrastive learning (LCCL), an adaptive Contrastive learning framework for source-free unsupervised domain adaptation, which encourages target domain samples to learn class-level discriminative features.
Journal ArticleDOI
A New Kinect V2-Based Method for Visual Recognition and Grasping of a Yarn-Bobbin-Handling Robot
Jing Han,Baoshun Liu,Yongle Jia,Shoufeng Jin,Maciej Sułowicz,Adam Glowacz,Grzegorz Królczyk,Zhixiong Li +7 more
TL;DR: A Kinect V2-based visual method to solve the human dependence on the yarn bobbin robot in the grabbing operation and shows that the average working time of the robot system is within 10 s, and the grasping success rate is above 80%, which meets the industrial production requirements.
Book ChapterDOI
Testing of Tight Crimped Joint Made on a Prototype Stand
TL;DR: In this paper, a project of a prototype solution of a device for making inseparable crimped joints consisting in forming the material of two elements to be joined to each other with the use of punches is presented.