| Application of BPANN in spinning deformation ofthin-walled tubular parts with longitudinal inner ribs |
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| 作者姓名： | 江树勇 李萍 薛克敏 |
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| 作者单位： | [1]SchoolofMaterialsScienceandEngineering,HarbinInstituteofTechnology,Harbin150001,China [2]SchoolofMaterialsScienceandEngineering,HefeiUniversityofTechnology,Anhui230000,China |
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| 基金项目： | Project (lc0 1c13 )supportedbytheOverseasReturneeFoundationoftheMinistryofEducationofChina |
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| 摘 要： | Back-propagation artificial neural network (BPANN) is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great influence on the height of inner ribs as well as fish scale on the surface of the spun part, a BPANN of 3-8-1 structure is established for predicting the height of inner rib and recognizing the fish scale defect. Experiments data have proved that the average relative error between the measured value and the predicted value of the height of inner rib is not more than 5%. It is evident that BPANN can not only predict the height of inner ribs of the spun part accurately,but recognize and prevent the occurrence of the quality defect of fish scale successfully, and combining BPANN with the ball backward spinning is essential to obtain the desired spun part.
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| 关 键 词： | 金属加工 纺纱 人工神经网络 价值标准 |
| 收稿时间： | 25 March 2003 |
| 修稿时间： | 6 July 2003 |
Application of BPANN in spinning deformation of thin-walled tubular parts with longitudinal inner ribs |
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| Jiang Shu-yong , Li Ping and Xue Ke-min.Application of BPANN in spinning deformation ofthin-walled tubular parts with longitudinal inner ribs[J].Journal of Central South University of Technology,2004,11(1):27-30. |
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| Authors: | Jiang Shu-yong Li Ping and Xue Ke-min |
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| Affiliation: | (1) School of Materials Science and Engineering, Harbin Institute of Technology, 150001 Harbin, China;(2) School of Materials Science and Engineering, Hefei University of Technology, 230000 Anhui, China |
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| Abstract: | Back-propagation artificial neural network (BPANN is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great influence on the height of inner ribs as well as fish scale on the surface of the spun part, a BPANN of 3-8-1 structure is established for predicting the height of inner rib and recognizing the fish scale defect. Experiments data have proved that the average relative error between the measured value and the predicted value of the height of inner rib is not more than 5%. It is evident that BPANN can not only predict the height of inner ribs of the spun part accurately,but recognize and prevent the occurrence of the quality defect of fish scale successfully, and combining BPANN with the ball backward spinning is essential to obtain the desired spun part. |
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| Keywords: | artificial neural network back-propagation ball spinning power spinning |
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