锻压技术

利用RBF神经网络预测反挤压连杆衬套过程中的挤压力

英文标题：Prediction of extrusion force in backward extruding connecting rod  bushing process by RBF neural network

单位：1.中北大学机械工程学院山西太原 030051 2.装甲军代局驻太原地区军代室山西太原 030006

分类号：TG391

出版年,卷(期):页码：2019,44(5):180-184

摘要：

采用单因素试验法，利用模拟软件Simufact进行了锡青铜连杆衬套反挤压试验，试验选取了挤压温度、凹模圆角半径和挤压比为试验因素，挤压力为评价指标。基于MATLAB软件，建立了挤压因素与挤压力之间的RBF神经网络模型，得到挤压温度、凹模圆角半径、挤压比和挤压力之间的非线性关系。通过试验数据进行RBF神经网络模型训练，然后再用训练好的RBF神经网络模型预测挤压力，并将预测的挤压力值与模拟的挤压力值做对比。结果表明：该神经网络模型能高精度地预测反挤压连杆衬套过程中的挤压力。

By using the singlefactor test method,the extrusion test of tin bronze connecting rod bushing was simulated by the simulation software Simufact, then the extrusion temperature, die radius and extrusion ratio were selected as the test factors, and the extrusion force was selected as the evaluation index. [JP3]Based on software MATLAB, the RBF neural network model of the relationship between extrusion factors and extrusion force was established，and the nonlinear relationships between extrusion temperature, die radius, extrusion ratio and extrusion force were obtained. The RBF neural network model was trained by the experiment data, and then the extrusion force was predicted by the trained RBF neural network model. In the end, the predicted extrusion force value was compared with the simulated extrusion force value. The results show that the neural network model can predict the extrusion force in the process of backward extruding connecting rod bushing with high precision.

基金项目：

山西省自然科学基金资助项目(2012011023-2)；山西省高校高新技术产业化项目(20120021)

作者简介：樊文欣（1964-），男，博士，教授 Email：fanwx@nuc.edu.cn

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