Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

被引：4
|
作者：
Shan, Kun [1 ]
Zhang, Yashuang [1 ]
Lan, Yingduo [1 ]
Jiang, Kaimeng [2 ]
Xiao, Guijian [2 ]
Li, Benkai [3 ]
机构：
[1] AECC Shenyang Liming Aeroengine Co Ltd, 6 Dongta St, Shenyang 110862, Peoples R China
[2] Chongqing Univ, Coll Mech & Vehicle Engn, 174 Shazheng St, Chongqing 400444, Peoples R China
[3] Qingdao Univ Technol, Sch Mech & Automot Engn, 777 Jialingjiang Rd, Qingdao 266520, Peoples R China
来源：
MATERIALS | 2023年 / 16卷 / 22期
关键词：
titanium alloy; abrasive belt grinding; roughness prediction; neural network;
D O I：
10.3390/ma16227224
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R2 value of 0.919.
引用
收藏
页数：14
相关论文
共 50 条
  • [21] The application of a radial basis function neural network for predicting the surface roughness in a turning process
    D.K. Sonar
    U.S. Dixit
    D.K. Ojha
    [J]. The International Journal of Advanced Manufacturing Technology, 2006, 27 : 661 - 666
  • [22] The radial basis function analysis of fire evacuation model based on RBF neural network
    Zhang, Lijie
    Liu, Jianchang
    Tan, Shubin
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 3): : S6417 - S6424
  • [23] The radial basis function analysis of fire evacuation model based on RBF neural network
    Lijie Zhang
    Jianchang Liu
    Shubin Tan
    [J]. Cluster Computing, 2019, 22 : 6417 - 6424
  • [24] Software Reliability Prediction Based on Radial Basis Function Neural Network
    Bal, Pravas Ranjan
    Mohapatra, Durga Prasad
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE, 2017, 509 : 101 - 110
  • [25] Modeling and prediction of surface roughness in belt polishing based on artificial neural network
    Qi, Junde
    Zhang, Dinghua
    Li, Shan
    Chen, Bing
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2018, 232 (12) : 2154 - 2163
  • [26] Spherical Head Surface Roughness Analysis and Experimental Research Based on the Abrasive Belt Grinding
    Zhang, Y.
    Huang, Y.
    Huang, Z.
    [J]. ADVANCES IN GRINDING AND ABRASIVE TECHNOLOGY XVI, 2011, 487 : 396 - 401
  • [27] Prediction of Abrasive Belt Wear Based on BP Neural Network
    Cao, Yuanxun
    Zhao, Ji
    Qu, Xingtian
    Wang, Xin
    Liu, Bowen
    [J]. MACHINES, 2021, 9 (12)
  • [28] Prediction of Parkinson's Disease Using Improved Radial Basis Function Neural Network
    Moorthy, Rajalakshmi Shenbaga
    Pabitha, P.
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (03): : 3101 - 3119
  • [29] Prediction of parkinson's disease using improved radial basis function neural network
    Moorthy, Rajalakshmi Shenbaga
    Pabitha, P.
    [J]. Computers, Materials and Continua, 2021, 68 (03): : 3101 - 3119
  • [30] Acoustic Emission Intelligent Prediction of Grinding Surface Roughness Based on BP Neural Network
    Guo, Li
    Ma, Yongfeng
    Wang, Yi
    Wang, Chong
    Liao, Zhenxing
    Li, Bo
    [J]. 2020 3RD WORLD CONFERENCE ON MECHANICAL ENGINEERING AND INTELLIGENT MANUFACTURING (WCMEIM 2020), 2020, : 373 - 376
12345