Development of an automated assembly process supported with an artificial neural network
Bobka P, Heyn J, Henningson JO, Römer M, Engbers T, Dietrich F, Dröder K (2018)
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2018
Journal
Publisher: Editorial Institution of Wrocaw Board of Scientific
Book Volume: 18
Pages Range: 28-41
Journal Issue: 3
DOI: 10.5604/01.3001.0012.4605
Abstract
A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.
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Germany (DE)How to cite
APA:
Bobka, P., Heyn, J., Henningson, J.O., Römer, M., Engbers, T., Dietrich, F., & Dröder, K. (2018). Development of an automated assembly process supported with an artificial neural network. Journal of Machine Engineering, 18(3), 28-41. https://dx.doi.org/10.5604/01.3001.0012.4605
MLA:
Bobka, Paul, et al. "Development of an automated assembly process supported with an artificial neural network." Journal of Machine Engineering 18.3 (2018): 28-41.
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