Optimization of Bowl Feeders Structure for Arbitrary Parts with Machine Learning
- Modern industrial production is heavily dependent on efficient workflow processes and automation. The steady flow of raw materials as well as the separation of vital parts and semi-finished products are at the core of these automated procedures. Commonly used systems for this work are bowl feeders, which separate the parts and material by a combination of mechanical vibration and friction. TheModern industrial production is heavily dependent on efficient workflow processes and automation. The steady flow of raw materials as well as the separation of vital parts and semi-finished products are at the core of these automated procedures. Commonly used systems for this work are bowl feeders, which separate the parts and material by a combination of mechanical vibration and friction. The production of these tools, especially the design of the ramping spiral, is delicate and time-consuming work, as the shape, slope, and material must be carefully adjusted for the corresponding parts. In this work, we propose an automated approach, making use of optimization procedures from artificial intelligence, to design the spiral ramps of the bowl feeders. Therefore, the whole system and considered parts are physically simulated and the optimized geometry is subsequently exported into a CAD system for the actual building, respectively printing. The employment of evolutionary optimization gives the need to develop a mathematical model for the whole setup and find an efficient representation of integral features.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8287 | Bibliografische Angaben |
Title (English): | Optimization of Bowl Feeders Structure for Arbitrary Parts with Machine Learning |
Conference: | International Conference on Smart and Sustainable Technologies (8. : 20-23 June 2023 : Split/Bol, Croatia) |
Author: | Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Jeremy Fischer |
Year of Publication: | 2023 |
Publisher: | IEEE |
First Page: | 1 |
Last Page: | 4 |
Parent Title (English): | 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech) |
ISBN: | 978-953-290-128-3 (Elektronisch) |
ISBN: | 979-8-3503-2320-7 (Print on Demand) |
DOI: | https://doi.org/10.23919/SpliTech58164.2023.10193444 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Collections of the Offenburg University: | Bibliografie |
Tag: | Optimization | Formale Angaben |
Relevance for "Jahresbericht über Forschungsleistungen": | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |