1、 第 1 页 外文资料 Application of Data Mining Method to Improve the Accuracy of Springback Prediction in Sheet Metal Forming Xu Jing ring(许京荆 )， ZHANG Zhi wei(张志伟 )， Wu， Yi min(吴益敏 ) School of Electrornechanical Engineering and Automation， Shanghai University，Shanghai 200072， P R China Abstract: A new meth
2、od 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 materia1 parameters Then according to the even design idea， the sui
3、table 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
4、)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 Key words :springback prediction， pa
5、ttern recognition， genetic algorithm， FEM ， even design idea， H0MR， data mining 第 2 页 1 Introduction The springback in sheet metal forming can be described as the change of sheet metal shape compared with the shape of the tools after forming process. Sheet metals with high strength-to-modulus ratio
6、such as high strength steels and aluminum alloys are particularly prone to springback， and these materials are becoming more important in automotive industry to reduce the car weight and increase fuel efficiency The final component shape does not conform to the tool geometry due to the springback of
7、 parts， so the die design becomes very difficult In order to compensate springback， die tryout is required in current automotive die development and construction process Die designs and construction is one of the most time-consuming steps in new car-type developing process Therefore， how to find an
8、effective and reliable method for springback prediction is of great significance Currently， the finite element method(FEM )has been used to calculate the springback of sheet metals in forming process2-7, but the results of calculation are not correct， and the calculation results of U shape component
9、 show that the average error is 62 . It cannot be directly used for die design There are a lot of nonlinear factors， such as large deformation, friction, impact and materia1 characters, which will influence the sheet metal forming and springback. Because the data mining technique is a good method to
10、 treat the complex factors and data, in order to predict the springback correctly in sheet metal forming , a new method is developed for springback prediction in the present paper The FEM is used to calculate the springback The computation results are compared with experimental data， the difference between the two results is taken as source data, and a new HOMR pattern recognition method of data mining is adopted for summarizing the calculation orderliness in FEM According to the hierarchical optimal map recognition method(HOMR)to build the
