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Multi-Objective Optimization of an Aerodynamic Feeding System Using Genetic Algorithm

Authors: Peter Nyhuis, Jan Busch

Abstract:

Considering the challenges of short product life cycles and growing variant diversity, cost minimization and manufacturing flexibility increasingly gain importance to maintain a competitive edge in today’s global and dynamic markets. In this context, an aerodynamic part feeding system for high-speed industrial assembly applications has been developed at the Institute of Production Systems and Logistics (IFA), Leibniz Universitaet Hannover. The aerodynamic part feeding system outperforms conventional systems with respect to its process safety, reliability, and operating speed. In this paper, a multi-objective optimisation of the aerodynamic feeding system regarding the orientation rate, the feeding velocity, and the required nozzle pressure is presented.

Keywords: Multi-objective optimization, Genetic Algorithm, aerodynamic feeding system

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338548

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References:


[1] Konak, A.; Coit, D. W.; Smith, A. E. (2006): Multi-objective optimization using genetic algorithms. A tutorial. In: Reliability Engineering & System Safety 91 (9), S. 992–1007.
[2] Fonseca, C. M.; Fleming, P. J. (1998): Multiobjective optimization and multiple constraints handling with evolutionary algorithms. I. A unified formulation. In: IEEE Trans. Syst., Man, Cybern. A 28 (1), S. 26–37.
[3] Belyaev, A.; Maag, V.; Speckert, M.; Obermayr, M.; Küfer, K.-H. (2015): Multi-criteria optimization of test rig loading programs in fatigue life determination. In: Engineering Structures 101, S. 16–23.
[4] Gen, M.; Ida, K.; Li, Y.; Kubota, E. (1995): Solving bicriteria solid transportation problem with fuzzy numbers by a genetic algorithm. In: Computers & Industrial Engineering 29 (1-4), S. 537–541.
[5] Murata, T.; Ishibuchi, H.; Tanaka, H. (1996): Multi-objective genetic algorithm and its applications to flowshop scheduling. In: Computers & Industrial Engineering 30 (4), S. 957–968.
[6] Deb, K.; Jain, P.; Gupta, N. K.; Maji, H. K. (2004): Multiobjective Placement of Electronic Components Using Evolutionary Algorithms. In: IEEE Trans. Comp. Packag. Technol. 27 (3), S. 480–492.
[7] Kumar, R.; Parida, P. P.; Gupta, M.: Topological design of communication networks using multiobjective genetic optimization. In: 2002 World Congress on Computational Intelligence - WCCI'02. Honolulu, HI, USA, 12-17 May 2002, S. 425–430.
[8] Busch, J.; Quirico, M.; Richter, L.; Schmidt, M.; Raatz, A.; Nyhuis, P. (2015): A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. In: CIRP Annals - Manufacturing Technology 64 (1), S. 5–8.
[9] Busch, J.; Knüppel, K. (2013): Development of a Self-Learning, Automatic Parameterisation of an Aerodynamic Part Feeding System. In: AMR 769, S. 34–41.
[10] Busch, J.; Schneider, S.; Knüppel, K.; Nyhuis, P. (2013): Identifying interactions in a feeding system. In: International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering 10, S. 931–937.