SETUP GENERATION USING NEURAL NETWORKS
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Published:2017-09-24
Issue:
Volume:5
Page:1169-1174
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ISSN:1805-9961
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Container-title:CBU International Conference Proceedings
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language:
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Short-container-title:CBUP
Author:
Mihaylov Oleg,Nikolcheva Galina,Popov Peter
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.
Publisher
CBU Research Institute
Subject
General Earth and Planetary Sciences,General Environmental Science