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| 利用BP网络预测板材力学性能的软件开发 | |
| 引用本文： | 郑晖,龚殿尧,王国栋,刘相华,张丕军.利用BP网络预测板材力学性能的软件开发[J].钢铁研究学报,2007,19(7):54-0. |
| 作者姓名： | 郑晖 龚殿尧 王国栋 刘相华 张丕军 |
| 作者单位： | 1. 沈阳航空工业学院航宇学院,辽宁沈阳110136;东北大学轧制技术及连轧自动化国家重点实验室,辽宁沈阳110004 2. 东北大学轧制技术及连轧自动化国家重点实验室,辽宁沈阳110004 3. 宝山钢铁股份有限公司产品所,上海200941 |
| 摘 要： | 针对热轧板带的力学性能预报问题，利用人工神经网络中的BP算法建立原始化学成分和热轧生产的主要工艺参数与产品力学性能之间的关系，并开发出专门的应用软件。软件共分3大部分：数据处理部分、人工神经网络训练部分、运用成熟网络预报部分。本软件的特点是直观、方便、稳定。数据均从稳定生产的现场取得。采用此软件对SS400钢的性能进行预报，经过10万次训练后，产品力学性能的预报值与实际值拟合良好，预报结果的相对误差很小。 |
| 关 键 词： | 人工神经网络 BP算法 力学性能 |
| 文章编号： | 1001-0963（2007）07-0054-05 |
| 收稿时间： | 1900-01-01; |
| 修稿时间： | 2007-03-15 |
Software of Predicting Mechanical Properties of Strip Steel by Using BP Networks |
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| ZHENG Hui,GONG Dian-yao,WANG Guo-dong,LIU Xiang-hua,ZHANG Pi-jun.Software of Predicting Mechanical Properties of Strip Steel by Using BP Networks[J].Journal of Iron and Steel Research,2007,19(7):54-0. | |
| Authors: | ZHENG Hui GONG Dian-yao WANG Guo-dong LIU Xiang-hua ZHANG Pi-jun |
| Affiliation: | 1. School of Aerospace Engineering, Shenyang Institute of Aeronautical Engineering, Shenyang 110136, Liaoning, China; 2. State Key Laboratory of Rolling Technology and Automation, Northeastern University, Shenyang 110004, Liaoning, China; 3. Product Research and Devlopment Center, Baoshan Iron and Steel Co Ltd, Shanghai 200941, China |
| Abstract: | In order to predict the mechanical properties of the hot rolled strip, the BP algorithm of ANN was used to reflect the influence of the chemical composition and the main parameters of hot rolling on the mechanical properties of product and the corresponding software was designed. This software has three parts., data, training part of the ANN, predicting part by using the mature ANN. The characteristic of this software is convenient, intuitive and stable. There is an example of predicting mechanical properties of SS400 steel using this software. On the basis of the data obtained randomly from the Bao Steel 2050 hot rolling supervisor, through one hundred thousand times training, the predicted results are in good agreement with the measured values. |
| Keywords: | artificial neural networks (ANN) BP algorithm mechanical properties |
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