[1]李兵, 姜海林, 刘奎武, 等. 基于 BP 人工神经网络的油箱端盖拉深成形仿真预测[J]. 锻压技术, 2017, 42 (11):177-180.
Li B, Jiang H L, Liu K W, et al. Prediction on deep drawing of fuel-tank end cap based on BP artificial neural network [J]. Forging & Stamping Technology, 2017, 42 (11):177-180.
[2]白建雄, 陈先朝, 王江南, 等. 304不锈钢壳变薄拉深的组织结构与性能[J]. 锻压技术, 2016, 40 (1):32-37.
Bai J X, Chen X C, Wang J N, et al. Microstructure and properties of stainless steel shell 304 in the ironing process [J]. Forging & Stamping Technology, 2016, 40 (1):32-37.
[3]李彦波, 刘荣丰, 刘红武, 等. 基于JSTAMP/NV的AA5042 薄壁筒形件变薄拉深仿真[J]. 计算机辅助工程, 2012, 21(6): 68-71.
Li Y B, Liu R F, Liu H W, et al. Ironing simulation on AA5042 thin wall cylindrical work-piece based on JSTAMP/NV [J]. Computer Aided Engineering, 2012, 21 (6): 68-71.
[4]许江平, 柳玉起, 章志兵, 等. 变薄拉深过程模拟的有限元动力显式算法[J]. 锻压技术, 2008, 33 (5):38-43.
Xu J P, Liu Y Q, Zhang Z B, et al. Finite element dynamic explicit method in ironing process simulation[J]. Forging & Stamping Technology, 2008, 33 (5): 38-43.
[5]王俊彪, 贾建军. 多道次变薄拉深的模拟与优化设计[J]. 西北工业大学学报, 1997, 15 (3): 348-354.
Wang J B, Jia J J. The simulation and optimization of multi-step ironing process[J]. Journal of Northwestern Polytechnic University, 1997, 15 (3): 348-354.
[6]肖善超. 弹壳多模一次连续变薄拉深工艺研究[D]. 秦皇岛: 燕山大学, 2012.
Xiao S C. Research on Multi-mode-one-off Ironing Process for Cartridge Case [D]. Qinhuangdao: Yanshan University,2012.
[7]平欣, 平鹏. 圆度、同轴度和圆柱度误差的最小二乘评定法[J]. 中国测试, 1997, (2): 44-46.
Ping X, Ping P. The least square estimation of roundness, coaxial and cylindrical errors[J]. China Measurement & Test, 1997,(2): 44-46.
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