为解决低压铸造铝合金车轮质量控制难度大的问题,采用遗传算法对工艺参数进行优化.基于铸造数值模拟结果,利用BP人工神经网络建立了铸造工艺参数与质量控制目标缩松缺陷和凝固时间的非线性关系,采用遗传算法实现了铸造工艺参数的优化.以某型低压铸造A356铝合金车轮为例,对浇注温度、上模温度、下模温度、侧模温度、模芯温度5个参数进行优化,得到的最佳工艺组合,可有效控制缩松缺陷和凝固时间.利用数值模拟结果、建立神经网络模型,采用遗传算法优化的方法,获得近似最优解,有助于优化低压铸造工艺.
参考文献
[1] | 胡红军,杨明波,罗静,王春欢,陈康.ProCAST软件在铸造凝固模拟中的应用[J].材料科学与工艺,2006(03):293-295. |
[2] | 李朝霞.人工神经网络及其在铸造工业中的应用[J].铸造,2000(01):31-35. |
[3] | Necat Altinkok;Rasit Koker .Neural network approach to prediction of bending strength and hardening behaviour of particulate reinforced (Al-Si-Mg)-aluminium matrix composites[J].Materials & Design,2004(7):595-602. |
[4] | A. Krimpenis;P. G. Benardos;G.-C. Vosniakos;A. Koukouvitaki .Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms[J].The International Journal of Advanced Manufacturing Technology,2006(5/6):509-517. |
[5] | prasad K. D. V. Yarlagadda;Eric Cheng Wei Chiang .A neural network system for the prediction of process parameters in pressure die casting[J].Journal of Materials Processing Technology,1999(0):583-590. |
[6] | D.H. Wu;M.S. Chang .Use of Taguchi method to develop a robust design for the magnesium alloy die casting process[J].Materials Science & Engineering, A. Structural Materials: Properties, Misrostructure and Processing,2004(1/2):366-371. |
[7] | Hasan Kurtaran;Babur Ozcelik;Tuncay Erzurumlu .Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm[J].Journal of Materials Processing Technology,2005(2):314-319. |
[8] | Shen Changyu;Wang Lixia;Li Qian .Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method[J].Journal of Materials Processing Technology,2007(2/3):412-418. |
上一张
下一张
上一张
下一张
计量
- 下载量()
- 访问量()
文章评分
- 您的评分:
-
10%
-
20%
-
30%
-
40%
-
50%