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基于BPGA的大锻件过渡区宏观成形性能优化
英文标题:Optimization on forming capacity for transition area of heavy forging based on BP-GA
作者:    戴甲洪     
单位:长江师范学院 
关键词:铝合金大锻件 局部加载 填充性 人工神经网络 遗传算法 
分类号:TG316
出版年,卷(期):页码:2017,42(3):180-184
摘要:

针对铝合金大型锻件局部加载成形工艺下过渡区筋部充填不满的问题,借助于BP神经网络和GA遗传算法与数值模拟技术相结合的策略对锻件的局部加载成形工艺参数进行了研究。以锻件过渡区填充性作为质量评价目标,建立了坯料温度、压力机加载速度、第1道次压下量、进给量与质量指标之间的关系模型,利用遗传算法求解得到了合理的工艺参数:坯料温度为435 ℃,压力机加载速度为5 mm·s-1,第1道次压下量为18 mm,进给量为1165 mm,并将得到的工艺参数进行再模拟,并应用于生产试制,生产后的锻件筋部过渡区充填完整,对比模拟结果,表明所提出的策略能有效对大型锻件局部加载成形过渡区充填问题进行控制。

For the problems of under filling for aluminum alloy heavy forging, the forming parameters under the local loading were studied by combining back propagation neural network and genetic algorithm with FEM. Then, the filling capacity of transition zone was regarded as evaluation objectives, a relationship model between billet temperature, press velocity, amount of depression, feed rate and quality index was established, and the reasonable parameters were obtained with billet temperature 435 ℃, press velocity 5 mm·s-1, amount of depression 18 mm and feed rate 1165 mm. The forming process was simulated again with the above obtained parameters, and the practical manufacture was carried out. Therefore, the capacity of filling was good. It can be concluded that the filling problem can be effectively managed by the strategy proposed.

基金项目:
作者简介:
肖 红(1989-),男,硕士,助教
参考文献:


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