| Data Mining方法在提高薄板冲压成型回弹预测准确性中的应用 |
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| 引用本文： | 许京荆,张志伟,吴益敏. Data Mining方法在提高薄板冲压成型回弹预测准确性中的应用[J]. 上海大学学报(英文版), 2004, 8(3): 348-353. DOI: 10.1007/s11741-004-0077-4 |
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| 作者姓名： | 许京荆 张志伟 吴益敏 |
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| 作者单位： | SchoolofElectromechanicalEngineeringandAutomation,ShanghaiUniversity,Shanghai200072,P.R.China |
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| 基金项目： | ProjectsupportedbyFord-ChinaResearchandDevelopmentFoundation (GrantNo .97162 14 |
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| 摘 要： | A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established.Based on the model, the calculation errors of springback can be controlled within 10 % compared with the experimental results.
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| 关 键 词： | 回弹预测 模式识别 遗传算法 FEM 数据挖掘 HOMR 薄板金属成型 分级最佳图谱识别算法 均等设计思想 |
| 收稿时间： | 2003-07-08 |
Application of data mining method to improve the accuracy of springback prediction in sheet metal forming |
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| Xu Jing-jing,Zhang Zhi-wei,Wu Yi-min. Application of data mining method to improve the accuracy of springback prediction in sheet metal forming[J]. Journal of Shanghai University(English Edition), 2004, 8(3): 348-353. DOI: 10.1007/s11741-004-0077-4 |
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| Authors: | Xu Jing-jing Zhang Zhi-wei Wu Yi-min |
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| Affiliation: | School of Electromechanical Engineering and Automation, Shanghai University, Shanghai 200072, P. R. China |
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| Abstract: | A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results. |
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| Keywords: | springback prediction pattern recognition genetic algorithm FEM even design idea HOMR data mining. |
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