Estimation of wire extension length using neural network in MIG welding
作者: Satoshi YamaneTetsuo YoshidaYasuyoshi KanekoHikaru YamamotoKenji Oshima
作者单位: 1Saitama University, Saitama, Japan
2Hitachi Construction Machinery Co. Ltd, Tokyo, Japan
刊名: Welding International, 2009, Vol.23 (7), pp.510-516
来源数据库: Taylor & Francis Journal
DOI: 10.1080/09507110802543013
关键词: wire extensionneural network modelMIG weldingwire melting phenomenaarc characteristicstransient responseradial base functionsigmoid function
原始语种摘要: Measuring arc length is important to obtain good welding quality in spite of variation of torch height. Therefore, it is necessary to detect arc behaviour in the transient state in addition to the steady state. For this purpose, this paper proposes neural network models which output the present wire extension from the data relating to wire melting, such as welding current, current pickup voltage and wire feed rate in every sampling period. Since performance of the neural network model depends on threshold functions, authors investigate the performance of the neural network models based on both sigmoid function (SF) and radial base function (RBF).To confirm the validity of these systems, fundamental experiments were carried out. The arc was directly observed and recorded as image data...
全文获取路径: Taylor & Francis  (合作)
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