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基于粒子群算法的大模数齿轮冷挤压变过盈量组合凹模优化设计
英文标题:Optimization design on cold extrusion changeable interference combined mould for large module gear based on particle swarm optimization
作者:陈莹莹 冯文杰 况智允 李栋 
单位:重庆理工大学 
关键词:大模数圆柱直齿轮 冷挤压 组合凹模 变过盈量 粒子群算法 
分类号:TG376
出版年,卷(期):页码:2017,42(8):122-128
摘要:

针对大模数圆柱直齿轮冷挤压过程中均匀过盈量组合凹模易出现开裂的问题,提出一种变过盈量组合凹模的设计方法,并以直径比、中圈内壁锥角和过盈系数为设计变量,以降低模芯内壁等效应力、避免模芯出现周向拉应力和避免内外圈塑性变形为优化目标,建立变过盈量组合凹模结构参数与综合加权评分值的Kriging模型。应用Kriging模型结合粒子群算法,在可行变量空间内寻优,得到最优工艺参数组合为:n2=1.55,n3=2.55,γ′=1.25°,β2=2.5‰,β3=2.4‰。研究结果表明,采用优化后的变过盈量组合凹模可有效避免组合凹模的破裂,提高模具的使用寿命。

For the crack problem of combined die in cold extrusion process of large module cylindrical spur gear, the design method of changeable interference combined mould was proposed. Then, the Kriging model with changeable interference combined mould structure parameters and integrated weighted score was established by design variables of the diameter ratio, the angle of inner wall of shrink ring and the interference coefficient and the optimization objective of reducing the equivalent stress of the inner wall of mould core, avoiding the circumferential tensile stress and plastic deformation of the inner and outer ring. The optimal process parameters with n2=1.55,n3=2.55,γ′=1.25°,β2=2.5‰,β3=2.4‰ in the variable space were found by the Kriging model combined with particle swam optimization. The research results show that the design method of the changeable interference combined mould can avoid the fracture of the combined mould and prolong the service life of mould.

基金项目:
重庆市教委科学技术研究资助项目(KJ1400944);重庆市科委资助项目(cstc2014yykfA60001)
作者简介:
陈莹莹(1979-),女,硕士,副教授
参考文献:


[1]洪慎章. 冷挤压实用技术[M]. 北京: 机械工业出版社, 2005.Hong S Z. Cold Extrusion Technology [M]. Beijing: China Machine Press, 2005.
[2]胡成亮, 袁兆峰, 蔡冰, 等. 齿形组合凹模弹性变形规律分析[J]. 锻压装备与制造技术, 2008, 43(5): 84-87.Hu C L, Yuan Z F, Cai B, et al. Elastic deformation rules of gear-shape combination die [J]. China Metalforming Equipment & Manufacturing Technology, 2008, 43(5): 84-87.
[3]胡成亮. 直齿轮冷精锻成形数值模拟分析与实验研究 [D]. 合肥: 合肥工业大学, 2005.Hu C L. Numerical Simulation and Experimental Research of Cold Net-shape Forming of Spur Gear [D]. Hefei: Heifei University of Technology, 2005.
[4]王强, 昌木松, 陈忠家, 等. 圆柱直齿轮挤压组合凹模的优化设计[J]. 合肥工业大学学报:自然科学版, 2008, 31(9): 1415-1418.Wang Q, Chang M S, Chen Z J, et al. Optimum design of combined extrusion die for cylindrical spur gear[J]. Journal of Heifei of Technology: Natural Science, 2008, 31(9):1415-1418.
[5]Eyercioglu O, Kutuk M A, Yilmaz N F. Shrink fit design for precision gear forging dies [J]. Journal of Materials Processing Technology, 2009, 209:2186-2194.
[6]Lee H C, Saroosh M A, Song J H, et al. The effect of shrink fitting ratios on tool life in bolt forming processes [J]. Journal of Materials Processing Technology, 2009, 209:3766-3775.
[7]张渝, 胡启国, 张甫仁. 基于粒子群算法的冷挤压模具结构参数优化设计[J]. 中国机械工程, 2010, 21(8): 1003-1007.Zhang Y, Hu Q G, Zhang F R. Optimum design of structural parameters of cold extrusion dies based on particle swarm optimization [J]. China Mechanical Engineering, 2010, 21(8):1003-1007.
[8]张渝, 周杰, 胡启国. 基于粒子群算法的预挤压组合凹模优化设计[J]. 热加工工艺, 2010, 39(3): 95-97.Zhang Y, Zhou J, Hu Q G. Optimization design of combined die for cold extrusion based on particle swarm optimization [J]. Hot Working Technology, 2010, 39(3):95-97.
[9]张渝. 基于代理模型的锻造模具结构智能优化研究[D]. 重庆: 重庆大学, 2009.Zhang Y. Forging-die Structure Intelligent Optimization Research Based on Surrogate Model [D]. Chongqing: Chongqing University, 2009.
[10]敖文刚, 伍太宾. 齿形组合凹模等强度分析及优化[J]. 塑性工程学报, 2016, 23(5): 36-43.Ao W G, Wu T B. Unified strength analysis and optimization of combined die for spur gear [J]. Journal of Plasticity Engineering, 2016, 23(5): 36-43.
[11]吴建民,陶菊春. 用综合加权评分法优化钻井泥浆配方的研究[J]. 农业工程学报, 2002, 18(2): 45-48.Wu J M, Tao J C. Experimental study on optimizing mud prescription for well drilling by using comprehensively weighted grading method [J]. Transactions of the CSAE, 2002, 18(2):45-48.
[12]Liu W, Yan Y Y. Multi-objective optimization of sheet metal forming process using pareto based genetic algorithm [J]. Journal of Materials Processing Technology, 2008, 208: 499-506.
[13]Palanivel R, Mathews P K. Prediction and optimization of process parameter of friction stir welded AA5083-H111 aluminum alloy using response surface methodology[J]. Journal of Central South University, 2012, 19(1): 1-8.
[14]Lophaven S N, Nielsen H B, Sondergaard J. DACE a matlab kriging toolbox [EB/OL].http://www.imm.dtu.dk/~hbn/dace/, 2007-11-07.
[15]孙新强, 谢延敏, 田银, 等. 基于小波神经网络和粒子群算法的铝合金板冲压回弹工艺参数优化[J]. 锻压技术, 2015, 40(1): 137-142.Sun X Q, Xie Y M, Tian Y, et al. Parameters optimization of stamping and springback for aluminum-alloy sheet based on wavelet neural network and particle swarm optimization algorithm [J]. Forging & Stamping Technology, 2015, 40(1):137-142.

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