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计算机系统应用英文版:2020,29(2):244-249
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基于卷积神经网络的平面磨削温度预测
(上海工程技术大学 机械与汽车工程学院,上海 201620)
Surface Grinding Temperature Prediction Based on Convolutional Neural Network
(School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China)
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Received:June 28, 2019    Revised:July 16, 2019
中文摘要: 为了减少磨削温度过高给零件带来热损伤等的负面影响,并提高零件产量、质量,本文建立了基于卷积神经网络的平面磨削温度预测模型.首先通过有限元仿真获得温度数据,并进行预处理,然后利用Google开源深度学习工具TensorFlow编写卷积神经网络程序,最后得到预测结果并与仿真值进行比较.结果表明,本文提出的基于卷积神经网络的磨削温度预测模型具有很强的学习能力以及非线性拟合能力,大大提高了磨削温度预测精度.
Abstract:In order to reduce the negative impact of excessive grinding temperature on the thermal damage of parts, and to improve the yield and quality of parts, this study establishes a surface grinding temperature prediction model based on convolutional neural network. Firstly, the temperature data is obtained through finite element simulation, and pre-processing is performed. Then, the convolutional neural network program is written by Google's open-end learning tool TensorFlow, and finally the prediction result is obtained and compared with the simulation value. The results show that the grinding temperature prediction model based on convolutional neural network has strong learning ability and nonlinear fitting ability, which greatly improves the prediction accuracy of grinding temperature.
文章编号:7267     中图分类号:    文献标志码:
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孙为钊,周俊.基于卷积神经网络的平面磨削温度预测.计算机系统应用,2020,29(2):244-249
SUN Wei-Zhao,ZHOU Jun.Surface Grinding Temperature Prediction Based on Convolutional Neural Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(2):244-249