汽车门框上条是变曲率的三维拉弯成形件，若门框上条设计不合理，拉弯成形过 程中零件曲率较大处易发生严重变形，影响汽车门框密封性，甚至导致密封条和尼槽无法 装配． 为了在产品开发前预测门框上条拉弯截面变形情况，缩短开发周期，降低设计成本， 本研究基于 ABAQUS 有限元软件，在拉弯成形回弹后的基础上，预测了门框上条的截面 变形情况，并提出加权均方误差( WMSE) 的方法，来评估拉弯件整个截面的变形情况． 最 后将该方法应用于某款乘用车门框上条6 种设计方案的择优选型． 结果表明:仿真预测的 截面变形与实验吻合较好;门框上条的弯曲角度对截面变形量影响较大，弯曲角度越小， 变形量越不明显． 

The locomotive loads distribution is dramatically influenced by the shims added to those springs． Considering the non-negligible nonlinear characteristics among locomotive bodies and secondary springs，a hybrid model is proposed by utilizing multi-output support vector regression as a compensation operator for the mechanism model． To improve the accuracy of compensation and obtain a set of optimal parameters，the genetic algorithm is used to globally optimize the parameters of the compensation operator． At the same time，the uniform design method is adopted to arrange the sampling scheme to reduce the sampling time． The proposed hybrid model is evaluated with the testing data set of a HXD1D high-power locomotive． The proposed model outperforms the mechanism model and the BP network hybrid model with respect to the root mean square error ( ＲMSE) ． An experimental result shows that the ＲMSE is reduced by 33. 53% in comparison with the mechanism model and 23. 69% less than that of the BP network model，which can further improve the accuracy of locomotive loads adjustment model． At the same time，the computation time of the proposed model is much less than that of the BP network hybrid model．