(武汉理工大学 机电工程学院，武汉 430074)
摘 要: 提出一种最小二乘支持向量机的Cu-0.75Cr铜合金反挤压力预测新模型。以断面缩减率、凸模锥角和挤压温度这3个主要工艺参数作为影响因素，以反挤压过程的挤压力为影响对象，通过最小二乘支持向量机模型建立影响因素和影响对象之间的复杂非线性关系。以正交实验数据为样本对模型进行训练，用训练好的模型预测在一定反挤压条件下Cu-0.75Cr铜合金的挤压力。结果表明：该模型不仅预测精度和处理速度大大高于人工神经网络预测模型，而且建模速度也比标准支持向量机快，实际预测误差小于3%。
关键字: Cu-0.75Cr铜合金；反挤压；挤压力；预测；最小二乘支持向量机
（School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430074, China）
Abstract:A novel prediction model for extrusion force of Cu-0.75Cr alloy reverse extrusion process based on least square support vector machine(LS-SVM) was proposed. With fault plane contraction rate, convex model awl angle and extrusion temperature as influence factors, and with extrusion force as influence object, the complex nonliner relations among the influence factors and influence object were fitted by LS-SVM model. Orthogonal experiment was taken to obtain data samples, and LS-SVM model was established through the data samples, so that the extrusion forces of Cu-0.75Cr alloy under different backward extrusion process conditions can be predicted by this model. The results show that not only the prediction accuracy and treatment speed by this model are much higher than those of artificial neural networks(ANN), but also the construction speed is higher than that of standard SVM, and the practical prediction errors are less than 3.0%.
Key words: Cu-0.75Cr alloy; backward extrusion process; extrusion force; prediction; least square support vector machine
