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タイトル: Cutting error prediction by multilayer neural networks for machine tools with thermal expansion and compression
著者: Nakayama, Kenji link image
Hirano, Akihiro link image link image
Katoh, Shinya
Yamamoto, Tadashi
Nakanishi, Kenichi
Sawada, Manabu
中山, 謙二
平野, 晃宏
発行日: 2002年 5月
出版社(者): IEEE(Institute of Electrical and Electronics Engineers)
雑誌名: Proceedings of the International Joint Conference on Neural Networks
巻: 2
開始ページ: 1373
終了ページ: 1378
キーワード: Machine tools
Thermal expansion
Error prediction
Numerical control machine
Thermal compression
Multilayer neural networks
抄録: In training neural networks, it is important to reduce input variables for saving memory, reducing network size, and achieving fast training. This paper proposes two kinds of selecting methods for useful input variables. One of them is to use information of connection weights after training. If a sum of absolute value of the connection weights related to the input node is large, then this input variable is selected. In some case, only positive connection weights are taken into account. The other method is based on correlation coefficients among the input variables. If a time series of the input variable can be obtained by amplifying and shifting that of another input variable, then the former can be absorbed in the latter. These analysis methods are applied to predicting cutting error caused by thermal expansion and compression in machine tools. The input variables are reduced from 32 points to 16 points, while maintaining good prediction within 6 ホシm, which can be applicable to real machine tools.
URI: http://hdl.handle.net/2297/6806
資料種別: Conference Paper
版表示: publisher
出現コレクション:1.査読済論文(工)

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