基于卷积神经网络的热轧带钢力学性能预报  

Mechanical property prediction of hot-rolled strip based on convolutional neural network

作　　者：胡石雄 李维刚 杨威[1] Hu Shixiong;Li Weigang;Yang Wei(Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China;National-provincial Joint Engineering Research Center of High Temperature Materials and Lining Technology,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉430081 [2]武汉科技大学高温材料与炉衬技术国家地方联合工程研究中心,湖北武汉430081

出　　处：《武汉科技大学学报》2018年第5期338-344,共7页Journal of Wuhan University of Science and Technology

基　　金：国家自然科学基金资助项目(51774219);武汉市青年科技晨光计划资助项目(2016070204010099)

摘　　要：为解决热轧带钢力学性能预报精度的问题,本文提出了一种将一维数值型数据转换为二维图像型数据的建模方法,基于LeNet-5和GoogLeNet卷积神经网络,构建了一种新型的热轧带钢力学性能预报模型,并利用实际生产数据对模型的适用性进行了测试。结果表明,所建模型的抗拉强度预报误差为2.49%,均方根误差为19.15 MPa,预测精度高于BP神经网络和单独的LeNet-5和GoogleNet卷积神经网络模型,所建模型的有效性和准确性均得到了验证。In order to improve the prediction accuracy of mechanical properties of hot-rolled strip,a modeling method for transforming one-dimensional numerical data into two-dimensional image data was proposed.Based on convolutional neural network LeNet-5 and GoogLeNet,a new type of model for mechanical property predition of hot-rolled strip was established,and its applicability was tested by employing the actual production data.The results reveal that the T s prediction error of the new model is 2.49%and the root mean square error is 19.15 MPa,which is more accurate than that of BP,LeNet-5 and GoogLeNet nerual network models.Therefore,the validity and accuracy of the established model have been verified.

关 键 词：热轧带钢 力学性能预报 卷积神经网络 LeNet-5 GoogLeNet 

分 类 号：TG335.5[金属学及工艺—金属压力加工]