摘要
针对熔模铸件模具型腔设计中存在收缩率赋值不准确导致多次修模的问题,作为初期研究,提出一种典型结构在凝固过程中收缩率的预测方法,为铸件收缩率预测提供一种思路。由于BP神经网络具有强大的容错性和鲁棒性,故基于BP神经网络构建依附于铸件结构的几何参数和收缩率之间的映射模型。由于BP神经网络隐含层神经元尚无针对不同案例的设计准则,因此,在映射模型建立时研究隐含层神经元个数对建模准确度的影响。结果表明,针对此典型结构铸件,当隐含层神经元个数为3时,映射模型的预测误差最小,此时,测试样本的预测和实测值收缩率平均偏差为0.09%,可较好地实现凝固过程收缩率预测。
In the design process of die cavity of investment casting, the inaccurate enlarged die cavity that based on shrinkage rate can lead the die need mold-repair for many times. As an initial study, a shrinkage rate prediction method of typical structure casting in the solidification process is proposed in this article. The method can provide a way of thinking for shrinkage rate prediction of casting. As BP neural network has strong fault tolerance and robustness function. Thus, the mapping model between geometric parameters that attach to the structure and shrinkage rate is built based on BP neural network. As there is no determination criterion for the number of the hidden layer neurons of the BP neural network in different cases, thus, the influence of the number of neurons in the hidden layer on the accuracy of modeling is researched. The result is that for the typical structure casting, when the number of neurons in the hidden layer is three, the mapping model has the least prediction error. In this case, the shrinkage rate average deviation of the predicted and measured values is 0.09%. The mapping model can better realize shrinkage rate prediction of the casting in solidification process.
出处
《航空制造技术》
2018年第9期47-51,70,共6页
Aeronautical Manufacturing Technology
关键词
收缩率
BP神经网络
预测方法
结构
铸件
Shrinkage rate
BP neural network
Prediction method
Structure
Casting
