完整後設資料紀錄
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dc.contributor.authorLin, H. L.en_US
dc.contributor.authorChou, C. P.en_US
dc.date.accessioned2017-04-21T06:49:39Z-
dc.date.available2017-04-21T06:49:39Z-
dc.date.issued2009en_US
dc.identifier.isbn978-1-4244-4869-2en_US
dc.identifier.issn2157-3611en_US
dc.identifier.urihttp://dx.doi.org/10.1109/IEEM.2009.5373032en_US
dc.identifier.urihttp://hdl.handle.net/11536/134931-
dc.description.abstractMany parameters affect the quality of the auto-brazing process. It is not easy to obtain optimal parameters of this process. This paper applies an integrated approach using the Taguchi method and a neural network (NN) to optimize the lap joint quality of air conditioner parts. The proposed approach consists of two phases. First phase executes initial optimization via Taguchi method to construct a database for the NN. In second phase, we use a NN with the Levenberg-Marquardt back-propagation (LMBP) algorithm to provide the nonlinear relationship between factors and the response based on the experimental data. Then, a well-trained network model is applied to obtain the optimal factor settings. The experimental results showed that the tensile strength of specimens of the optimal parameters via the proposed approach is better than apply Taguchi method only.en_US
dc.language.isoen_USen_US
dc.subjectTaguchi methoden_US
dc.subjectneural networken_US
dc.subjectbrazingen_US
dc.titleOptimizing the Auto-brazing Process Quality via a Taguchi-Neural Network Approach in the Automotive Industryen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/IEEM.2009.5373032en_US
dc.identifier.journal2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4en_US
dc.citation.spage1347en_US
dc.citation.epage+en_US
dc.contributor.department機械工程學系zh_TW
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.identifier.wosnumberWOS:000280236601021en_US
dc.citation.woscount2en_US
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