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pro vyhledávání: '"Schönhof, Raoul"'
When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capability is challenging, since they can be very difficult to simulate, are t
Externí odkaz:
http://arxiv.org/abs/2303.12739
In the domain of computer vision, deep residual neural networks like EfficientNet have set new standards in terms of robustness and accuracy. One key problem underlying the training of deep neural networks is the immanent lack of a sufficient amount
Externí odkaz:
http://arxiv.org/abs/2202.10099
Autor:
Schönhof, Raoul, Fechter, Manuel
Publikováno v:
Procedia CIRP Volume 91, 2020, Pages 433-438
Aiming for a higher economic efficiency in manufacturing, an increased degree of automation is a key enabler. However, assessing the technical feasibility of an automated assembly solution for a dedicated process is difficult and often determined by
Externí odkaz:
http://arxiv.org/abs/2202.04051
Publikováno v:
Procedia CIRP Volume 93, 2020, Pages 1429-1434
Focus of this work is to recognize standards and further features directly from 3D CAD models. For this reason, a neural network was trained to recognize nine classes of machine elements. After the system identified a part as a standard, like a hexag
Externí odkaz:
http://arxiv.org/abs/2202.00573
Autor:
Schönhof, Raoul, Werner, Artem, Elstner, Jannes, Zopcsak, Boldizsar, Awad, Ramez, Huber, Marco
Publikováno v:
2021, Procedia CIRP 100(7):331-336
Not only automation of manufacturing processes but also automation of automation procedures itself become increasingly relevant to automation research. In this context, automated capability assessment, mainly leveraged by deep learning systems driven
Externí odkaz:
http://arxiv.org/abs/2201.12107
Publikováno v:
In Journal of Dentistry July 2024 146
Autor:
Schönhof, Raoul, Werner, Artem, Elstner, Jannes, Zopcsak, Boldizsar, Awad, Ramez, Huber, Marco
Publikováno v:
In Procedia CIRP 2021 100:331-336
Publikováno v:
In Procedia CIRP 2020 93:1429-1434
Publikováno v:
In Procedia CIRP 2020 91:237-242