Gentelligent processes in biologically inspired manufacturing

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Denkena, B.; Dittrich, M.-A.; Stamm, S.; Wichmann, M.; Wilmsmeier, S.: Gentelligent processes in biologically inspired manufacturing. In: CIRP Journal of Manufacturing Science and Technology 32 (2021), S. 1-15. DOI:

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Sum total of downloads: 117

Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time. © 2020 The Authors
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2021
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 76 64.96%
2 image of flag of United States United States 18 15.38%
3 image of flag of China China 7 5.98%
4 image of flag of No geo information available No geo information available 5 4.27%
5 image of flag of Taiwan Taiwan 2 1.71%
6 image of flag of Israel Israel 2 1.71%
7 image of flag of Thailand Thailand 1 0.85%
8 image of flag of Namibia Namibia 1 0.85%
9 image of flag of India India 1 0.85%
10 image of flag of Switzerland Switzerland 1 0.85%
    other countries 3 2.56%

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