Prediction of Abrasive Belt Wear Based on BP Neural Network

被引:7
|
作者
Cao, Yuanxun [1 ]
Zhao, Ji [1 ]
Qu, Xingtian [1 ]
Wang, Xin [1 ]
Liu, Bowen [1 ]
机构
[1] Jilin Univ, Sch Mech & Aerosp Engn, Changchun 130025, Peoples R China
基金
中国国家自然科学基金;
关键词
abrasive belt; wear; grinding; prediction; BP neural network;
D O I
10.3390/machines9120314
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Research on wind power Prediction based on BP neural Network
    Hu, Dongmei
    Zhang, Zhaoyun
    Zhou, Hao
    [J]. 2022 2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022, 2022,
  • [32] Angular Velocity Prediction of GFSINS Based on BP Neural Network
    Duan Hai-qing
    Zhu Qi-dan
    [J]. MANAGEMENT, MANUFACTURING AND MATERIALS ENGINEERING, PTS 1 AND 2, 2012, 452-453 : 846 - 852
  • [33] Prediction of Alloy Yield Based on Optimized BP Neural network
    Huang, Shan
    Huang, Xinhao
    Weng, Xiaona
    Ma, Liyuan
    Sun, Zhiyu
    [J]. 2019 5TH INTERNATIONAL CONFERENCE ON GREEN POWER, MATERIALS AND MANUFACTURING TECHNOLOGY AND APPLICATIONS (GPMMTA 2019), 2019, 2185
  • [34] Prediction of magnetic body top based on BP neural network
    Zhao W.
    Liu Y.
    Tao D.
    Zhao L.
    Hu W.
    [J]. Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting, 2020, 55 (05): : 1139 - 1148
  • [35] Yarn Quality Prediction Based on Improved BP Neural Network
    Yang Jian-guo
    Xiong Jing-wei
    Xun Lan
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS, MECHATRONICS AND INTELLIGENT SYSTEMS (AMMIS2015), 2016, : 686 - 693
  • [36] Prediction of Zenith Tropospheric Delay Based on BP Neural Network
    Wang, Yong
    Zhang, Lihui
    Yang, Jing
    [J]. ADVANCES IN COMPUTER SCIENCE AND EDUCATION, 2012, 140 : 459 - +
  • [37] Research on tax prediction model based on BP neural network
    Zhang, Shaoqiu
    Hu, Yueming
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2006, 13 : 330 - 333
  • [38] A ROP PREDICTION APPROACH BASED ON IMPROVED BP NEURAL NETWORK
    Duan, Jinan
    Zhao, Jinhai
    Xiao, Li
    Yang, Chuanshu
    Chen, Huinian
    [J]. 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), 2014, : 668 - 671
  • [39] Prediction of Fabric Drape Based on BP Neural Network Paper
    Xia, Shuhui
    Fan, Xiujuan
    [J]. 2020 IEEE 7TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND APPLICATIONS (ICIEA 2020), 2020, : 206 - 210
  • [40] Research on wind power Prediction based on BP neural Network
    Hu, Dongmei
    Zhang, Zhaoyun
    Zhou, Hao
    [J]. 2022 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, COMPUTING, COMMUNICATION AND SUSTAINABLE TECHNOLOGIES (ICAECT), 2022,