铝合金复杂汽车零部件热成形-淬火一体化工艺参数优化（英文）
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摘要
铝合金热成形-淬火一体化工艺将成形和热处理结合,能同时实现零件尺寸精度和性能的控制,是实现汽车轻量化的主要途径之一。但是在冲压成形尺寸相对较大、形状相对较复杂的铝合金板件时,依然存在各工艺参数难调试的问题。建立了AA6061铝合金的热变形统一黏塑性损伤本构模型,将其用于铝合金汽车B柱热冲压成形模拟,采用BP神经网络构建了工艺参数(板料成形温度,冲压速度,模具间隙)与成形性(最大减薄率,最大增厚率)之间的关系并结合遗传算法实现多目标优化,得到了AA6061铝合金热冲压的最佳成形工艺参数。优化之后,B柱的最大减薄率和最大增厚率分别从56.5%和14.2%降到了13.0%和10.0%,通过优化之后的工艺参数,制得了成形性良好、尺寸精度高的零件。结果证明了基于神经网络和遗传算法的热成形工艺优化方法的可行性和有效性。
To obtain appropriate forming parameters, a thermo-mechanical finite element(FE) model using a unified viscoplastic damage model was set up to predict the formability of a complex-designed 6 xxx aluminum alloy B-pillar. A back propagation(BP) neural-network combined with multi-objective genetic algorithm(GA) method was adopted to optimize the key process variables including blank temperature, stamping speed and die clearance during hot stamping process. The results show that after optimization, the thinning and thickening rates are reduced to 13.0% and 10.0% compared with the initial 56.5% and 14.2%, respectively. In addition, a successful hot stamping B-pillar with satisfactory mechanical performance and excellent forming accuracy is achieved experimentally using the optimized parameters, indicating that the finite element model can simulate the hot stamping process accurately, and that the optimization method utilized in this paper is feasible and effective.
To obtain appropriate forming parameters, a thermo-mechanical finite element(FE) model using a unified viscoplastic damage model was set up to predict the formability of a complex-designed 6 xxx aluminum alloy B-pillar. A back propagation(BP) neural-network combined with multi-objective genetic algorithm(GA) method was adopted to optimize the key process variables including blank temperature, stamping speed and die clearance during hot stamping process. The results show that after optimization, the thinning and thickening rates are reduced to 13.0% and 10.0% compared with the initial 56.5% and 14.2%, respectively. In addition, a successful hot stamping B-pillar with satisfactory mechanical performance and excellent forming accuracy is achieved experimentally using the optimized parameters, indicating that the finite element model can simulate the hot stamping process accurately, and that the optimization method utilized in this paper is feasible and effective.
引文
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23 Mohamed S M,Foster A D,Lin J G et al.International Journal of Machine Tools&Manufacture[J],2012,53(1):27
24 Ma W,Wang B,Bian J et al.Metallurgical&Materials Transactions A[J],2015,46(6):2748
25 Lorenz D.Simulation of Thermo-Mechanical Forming Process with LS-DYNA[M].Bamberg:LS-DYNA Forum,2010
26 Zhou J,Wang B Y,Lin J G et al.Transactions of Nonferrous Metals Society of China[J],2014,24(11):3611
27 Karbasian H,Tekkaya A E.Journal of Materials Processing Technology[J],2010,210(15):2103
28 Younesi M,Bahrololoom M E,Ahmadzadeh M.Computational Materials Science[J],2010,47(3):645
29 Fu Z,Mo J,Chen L et al.Materials&Design[J],2010,31(1):267
30 Dehghani K,Nekahi A.Materials&Design[J],2010,31(4):2224
31 Yu W,Li M Q,Luo J et al.Materials&Design[J],2010,31(7):3282
32 Li W,Zhan L,Zhao J.The Chinese Journal of Nonferrous Metals[J],2016,26(6):1159(in Chinese)
33 Harrison N R,Luckey S G.Sae International Journal of Materials&Manufacturing[J],2014,7(3):567
2 Zhang J X,Zhang K L,Liu Y T et al.Rare Metals[J],2014,33(4):404
3 Zhang J X,Fan J X,Liu Y T et al.Rare Metals[J],2015,34(6):387
4 Hu Z L,Wang X S,Pang Q et al.International Journal of Advanced Manufacturing Technology[J],2015,80(5-8):959
5 Fan X B,Zhu-Bin H E.Transactions of Nonferrous Metals Society of China[J],2012,22:S389
6 Toros S,Ozturk F,Kacar I.Journal of Materials Processing Technology[J],2008,207(1-3):1
7 Mahabunphachai S,Ko?M.Materials&Design[J],2010,31(5):2422
8 Golovashchenko S,Krause A.Journal of Materials Engineering&Performance[J],2005,14(4):503
9 Kleiner M,Geiger M,Klaus A.CIRP Annals-Manufacturing Technology[J],2003,52(2):521
10 Lin J,Dean TA,Garrett R P et al.UK Patent,WO/2008/059242[P],2008
11 Fan X,He Z,Zheng K et al.Materials&Design[J],2015,83:557
12 Foster A D,Mohamed M S,Lin J et al.Steel Research International[J],2008,79(2):113
13 Mohamed M S.An Investigation of Hot Forming Quench Process for AA6082 Aluminium Alloys[D].London:Imperial College London,2010
14 Fan X,He Z,Yuan S et al.Materials Science&Engineering A[J],2013,573(18):154
15 Fan X B,He Z B,Zhou W X et al.Journal of Materials Processing Technology[J],2016,228:179
16 Wang L,Strangwood M,Balint D et al.Materials Science&Engineering A[J],2011,528(6):2648
17 Ma W Y,Wang B Y,Lei F U et al.Transactions of Nonferrous Metals Society of China[J],2015,25(7):2342
18 Zhou Jing,Wang Baoyu,Lin Jianguo et al.Archives of Civil and Mechanical Engineering[J],2013,13(3):401
19 Xiao W,Wang B,Zhou J et al.Engineering Optimization[J],2016,48(12):2173
20 Park D H,Ko B C,Yoo Y C.Journal of Materials Science[J],2002,37(8):1593
21 Soundararajan V,Zekovic S,Kovacevic R.International Journal of Machine Tools&Manufacture[J],2005,45(14):1577
22 Lin J.Fundamentals of Material Modelling for Metals Processing Technologies[M].London:Imperial College Press,2015
23 Mohamed S M,Foster A D,Lin J G et al.International Journal of Machine Tools&Manufacture[J],2012,53(1):27
24 Ma W,Wang B,Bian J et al.Metallurgical&Materials Transactions A[J],2015,46(6):2748
25 Lorenz D.Simulation of Thermo-Mechanical Forming Process with LS-DYNA[M].Bamberg:LS-DYNA Forum,2010
26 Zhou J,Wang B Y,Lin J G et al.Transactions of Nonferrous Metals Society of China[J],2014,24(11):3611
27 Karbasian H,Tekkaya A E.Journal of Materials Processing Technology[J],2010,210(15):2103
28 Younesi M,Bahrololoom M E,Ahmadzadeh M.Computational Materials Science[J],2010,47(3):645
29 Fu Z,Mo J,Chen L et al.Materials&Design[J],2010,31(1):267
30 Dehghani K,Nekahi A.Materials&Design[J],2010,31(4):2224
31 Yu W,Li M Q,Luo J et al.Materials&Design[J],2010,31(7):3282
32 Li W,Zhan L,Zhao J.The Chinese Journal of Nonferrous Metals[J],2016,26(6):1159(in Chinese)
33 Harrison N R,Luckey S G.Sae International Journal of Materials&Manufacturing[J],2014,7(3):567