Optimization of Satmping Temperature in Nonisothermal Stamping of AZ31B Magnesium Alloy
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摘要: 为了获得更利于镁合金差温成形的模具温度与板料温度，采用量子遗传算法（QGA）和支持向量回归机（SVR）相结合的寻优方法用于温度优化。基于镁合金差温成形有限元模型，建立模具各部件、板料的温度与成形件目标区域厚度之间的SVR模型，通过量子遗传算法对建立的近似模型寻优以获得最适合镁合金AZ31B差温成形的温度参数。以NUMISHEET2011中的十字杯形件为研究对象，利用优化后的温度进行差温成形仿真并与试验数据值对比。结果表明，优化后的温度能够使得板料厚度分布更均匀。Abstract: In order to obtain the preferable mould temperature and sheet temperature in the nonisothermal stamping of magnesium alloy, Quantum genetic algorithm (QGA) and support vector regression machine (SVR) were adopted to optimize temperature. The SVR surrogate model was estabilished between the parts of temperature and the target area thickness of forming part via finite element model. The surrogate model is optimized with the quantum genetic algorithm to obtain the optimal temperature parameters in the nonisothermal stamping of AZ31B magnesium alloy. Taking NUMISHEET2011 cross-shaped cup part for an example, the optimal temperature was adopted for simulation. Comparing the results with test data, the optimal temperature can effectively improve the thickness uniformity.
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Key words:
- nonisothermal stamping /
- temperature /
- genetic algorithm /
- optimization
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表 1 AZ31B材料参数
杨氏模量 45 GPa 泊松比 0.35 密度 1 770 kg/m3 比热 1 000 kg/(J·℃) 热传导率 96 W/(m·℃) 接触换热系数 4 500 W/(m·℃) 
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表 2 拉丁超立方抽样结果
序号 各部件温度/℃ T1 T2 T3 T4 1 113.42 92.89 251.98 216.45 2 89.74 102.37 275.65 267.78 3 97.63 97.63 228.30 255.93 4 99.21 100.79 232.23 240.13 5 103.95 86.58 255.93 232.23 6 115.00 113.42 220.40 287.50 7 102.37 91.32 259.88 212.50 8 105.53 108.68 283.55 251.98 9 111.84 115.00 236.18 244.08 10 86.58 96.05 216.45 220.40 11 88.16 110.26 224.35 259.88 12 110.26 88.16 271.70 275.65 13 96.05 111.84 279.60 248.03 14 91.32 105.53 263.83 228.30 15 107.11 89.74 248.03 279.60 16 108.68 85.00 212.50 224.35 17 94.47 99.21 287.50 283.55 18 85.00 107.11 240.13 271.70 19 92.89 94.47 244.08 236.18 20 100.79 103.95 267.78 263.83
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表 3 温度优化前后板料厚度对比
名称 优化前 优化后 圆角1厚度/mm 0.465 0.462 圆角2厚度/mm 0.402 0.471 圆角3厚度/mm 0.465 0.462 均匀性误差/% 16.8 2.4
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