【作者】 齐乐华； 史忠科； 侯俊杰； 李贺军；

【Author】 Le-Hua Qi, Zhong-Ke Shi, Jun-Jie Hou, He-Jun Li College of Mechanical and Electrical Engineering, Northwestern Polytechnical University, Xi’an, 710072

【机构】 西北工业大学；

【摘要】 针对液-固挤压复合材料棒材成形时工艺参数难于选取、试验工作量大的问题,采用人工神经网络技术与试验相结合的方法,通过对样本处理、神经网络模型参数及收敛性等进行分析,建立了工艺参数ANN预测模型,可以对复合材料液-同挤压成形的关键工艺参数进行预测,预测值与试验值吻合较好,最大误差不超过0.72%,为复合材料液-固挤压成形的应用开辟了有效途径。更多还原

【Abstract】 It is difficult to determine the process parameters for forming composite bar products by liquid-solid extrusion and generally a lot of experiments are required. For solving this problem, the artificial neural network forecasting model of the process parameters has been established by combining with experiment methods in this paper. In the same time, some techniques including disposal of sample data, selection of neural network model parameters and convergent profile have been investigated. By the established model, the key process parameters for extruding composite bar products have been forecasted. The results are well coincident with the experimental values, and the error is not larger than 0.72%, which prove the method and established model are efficient and practical.更多还原