为提高铝合金压铸件晶粒尺寸预测的效率和准确率,应用遗传算法-极限学习机(GA-ELM)模型预测晶粒尺寸。ELM的输入层权值矩阵及隐含层阈值矩阵具有随机性,通过GA算法对ELM的输入层权值矩阵和隐含层阈值矩阵进行优化,建立GA-ELM模型。以晶粒尺寸作为输出参数,相关压铸工艺参数作为输入参数,通过压铸生产实验及金相测量获得相应数据,对GA-ELM模型进行实例分析,并与同样使用遗传算法优化的GA-BP神经网络模型和原始ELM模型预测结果进行对比。最后,通过金相组织测量实验验证GA-ELM模型预测结果的可靠性。结果表明,利用GA-ELM模型预测铝合金压铸件晶粒尺寸具有较高的预测精度及预测效率,与其它算法相比,具有一定的优越性。

Effective grain size prediction for aluminum alloy die castings is of great significance to the rational formulation of die casting process parameters and to the improvement of casting mechanical properties. The traditional grain size prediction method cannot give consideration to both the efficiency and accuracy because of its inherent defects. To improve the efficiency and accuracy of predicting grain size for aluminum alloy die castings, this study proposes a prediction method that is based on the genetic algorithm-extreme learning machine (GA-ELM) model. ELM has the characteristics of few parameter settings, fast learning and good generalization performance, but the algorithm randomly generates the initial input layer weight matrix and the hidden layer threshold matrix, which greatly affects the prediction result. By exploiting GA's excellent global optimization ability, the optimal initial input layer weight matrix and the hidden layer threshold matrix for ELM can be found. The establishment of GA-ELM model can considerably improve the prediction accuracy of ELM model. This study uses grain size as the output parameters and relevant die casting process parameters as the input parameters. The castings produced under different die-casting process parameters are obtained experimentally, and the microstructures of specified sections of key casting positions are analyzed and measured to obtain the average grain size of the sec tions, i.e. the output parameters. The GA-ELM model is trained and tested using these data. To verify the superiority of the GA-ELM model in grain size prediction, this study compares the prediction results of GA-ELM model with the GA-BP neural network model and the original ELM model, and eventually verifies the reliability of GA-ELM model prediction results through metallographic structure measurement experiment. The results show that the GA-ELM model has higher prediction accuracy than the GA-BP neural network model and the original ELM model. Besides, its prediction efficiency is higher than the GA-BP model, while is lower than the original ELM model. With fairly high prediction accuracy and efficiency, the GA-ELM model can meet the actual engineering requirements. Furthermore, its prediction reliability and excellent prediction effect are verified by the results of metallographic structure measurement experiment.