SETUP GENERATION USING NEURAL NETWORKS

  • Oleg Mihaylov Faculty of Industrial Technology, Technical University of Sofia, Bulgaria
  • Galina Nikolcheva Faculty of Industrial Technology, Technical University of Sofia, Bulgaria
  • Peter Popov Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria
DOI: https://doi.org/10.12955/cbup.v5.1090
Keywords: neural networks, setup generation, interacting features, technological process

Abstract

The article presents an unsupervised learning algorithm that groups technological features in a setup for machining process. Setup generation is one of the most important tasks in automated process planning and in fixture configuration. A setup is created based on approach direction of the features.  The algorithm proposed in this work generates a neural network that determines the setup each feature belongs to, and the number of setups generated is minimal. This algorithm, unlike others, is not influenced by the order of the input sequence. Parallel implementation of the algorithm is straightforward and can significantly increase the computational performance.

References

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Published
2017-09-24
Issue
Vol 5 (2017): CBU International Conference Proceedings 2017
Section
Natural Sciences and ICT
Copyright (c) 2017 Oleg Mihaylov, Galina Nikolcheva, Peter Popov

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