Gentelligent processes in biologically inspired manufacturing

Show simple item record

dc.identifier.uri Denkena, Berend Dittrich, Marc-André Stamm, Siebo Wichmann, Marcel Wilmsmeier, Sören 2021-03-26T08:44:48Z 2021-03-26T08:44:48Z 2021
dc.identifier.citation 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:
dc.description.abstract 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 eng
dc.language.iso eng
dc.publisher Amsterdam [u.a.] : Elsevier
dc.relation.ispartofseries CIRP Journal of Manufacturing Science and Technology 32 (2021)
dc.rights CC BY-NC-ND 4.0 Unported
dc.subject bio-inspired manufacturing eng
dc.subject biologicalisation eng
dc.subject process planning eng
dc.subject production scheduling eng
dc.subject.ddc 600 | Technik ger
dc.title Gentelligent processes in biologically inspired manufacturing
dc.type Article
dc.type Text
dc.relation.essn 1878-0016
dc.relation.issn 1755-5817
dc.bibliographicCitation.volume 32
dc.bibliographicCitation.firstPage 1
dc.bibliographicCitation.lastPage 15
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

Files in this item

This item appears in the following Collection(s):

Show simple item record


Search the repository


My Account

Usage Statistics