SETUP GENERATION USING NEURAL NETWORKS

Oleg Mihaylov (), Galina Nikolcheva () and Peter Popov ()
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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

CBU International Conference Proceedings, 2017, vol. 5, issue 0, 1169-1174

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.

Keywords: neural networkssetup generation; interacting features; technological process (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:aad:iseicj:v:5:y:2017:i:0:p:1169-1174

DOI: 10.12955/cbup.v5.1090

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