TY - JOUR

T1 - Gentelligent processes in biologically inspired manufacturing

AU - Denkena, Berend

AU - Dittrich, Marc André

AU - Stamm, Siebo

AU - Wichmann, Marcel

AU - Wilmsmeier, Sören

N1 - Funding Information: The results presented in this paper were obtained within in the Collaborative Research Center 653 “Gentelligent Components in their Lifecycle” Subproject K2, the transfer projects T09, T10 and T13 and the DFG transfer project BO 3523/6-1 of the priority program (SPP) 1180. The authors would like to thank the German Research Foundation (DFG) for its financial support (Grant SFB653, No. 5486368 , Grant Transfer SPP 1180, No. 632003 ).

PY - 2021/1

Y1 - 2021/1

N2 - 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.

AB - 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.

KW - Bio-inspired manufacturing

KW - Biologicalisation

KW - Process planning

KW - Production scheduling

UR - http://www.scopus.com/inward/record.url?scp=85097350268&partnerID=8YFLogxK

U2 - 10.1016/j.cirpj.2020.09.015

DO - 10.1016/j.cirpj.2020.09.015

M3 - Article

AN - SCOPUS:85097350268

VL - 32

SP - 1

EP - 15

JO - CIRP Journal of Manufacturing Science and Technology

JF - CIRP Journal of Manufacturing Science and Technology

SN - 1755-5817

ER -