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pro vyhledávání: '"Meurer, Markus"'
Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prom
Externí odkaz:
http://arxiv.org/abs/2407.01211
As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using
Externí odkaz:
http://arxiv.org/abs/2407.01200
Tool wear conditions impact the final quality of the workpiece. In this study, we propose a deep learning approach based on a convolutional neural network that incorporates cutting conditions as extra model inputs, aiming to improve tool wear estimat
Externí odkaz:
http://arxiv.org/abs/2407.01199
Publikováno v:
In CIRP Annals - Manufacturing Technology 2024 73(1):57-60
Publikováno v:
In Procedia CIRP 2024 122:671-676
Akademický článek
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Publikováno v:
In CIRP Journal of Manufacturing Science and Technology July 2023 43:1-14
Autor:
Tekkaya, Berk, Meurer, Markus, Dölz, Michael, Könemann, Markus, Münstermann, Sebastian, Bergs, Thomas
Publikováno v:
In Journal of Materials Processing Tech. January 2023 311
Publikováno v:
In Procedia CIRP 2023 118:584-589
Publikováno v:
In Procedia CIRP 2023 118:489-494