Prediction model for bearing surface friction coefficient in bolted joints based on GA-BP neural network and experimental data

被引：10
作者：
Chen, Wentao [1 ,2 ]
Li, Ying [1 ,2 ]
Liu, Zhifeng [1 ,3 ,4 ]
Zhang, Caixia [1 ,2 ]
Zhao, Yongsheng [1 ,2 ]
Yan, Xing [1 ,2 ]
机构：
[1] Beijing Univ Technol, Beijing Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Inst Adv Mfg & Intelligent Technol, Beijing 100124, Peoples R China
[3] Jilin Univ, Key Lab CNC Equipment Reliabil, Minist Educ, Changchun 130025, Jilin, Peoples R China
[4] Key Lab Adv Mfg & Intelligent Technol High End CNC, Changchun 130025, Jilin, Peoples R China
来源：
TRIBOLOGY INTERNATIONAL | 2025年 / 201卷
基金：
中国国家自然科学基金;
关键词：
Friction coefficient; Bolted joints; Surface topography; GA-BP prediction model; THREADED FASTENERS; TORQUE; CONTACT; PERFORMANCE; PRELOAD;
D O I：
10.1016/j.triboint.2024.110217
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
A method has been developed to quantify the bearing surface friction coefficient in bolted joints based on the mechanics of the tightening process. By measuring the surface topography under different tightening torques, a strong correlation between the variation of the microscopic topography and the friction coefficient was revealed. The friction coefficient initially decreases and subsequently increases due to surface wear and microstructural transformations. A genetic algorithm (GA) and backpropagation neural network (BP) were employed to create a predictive model, optimizing network parameters for enhanced learning efficiency and accuracy. The experimentally validated GA-BP prediction model forecasts friction behavior with a maximum relative error of 3.57 %, offering valuable insights for the design and optimization of bolted joints.
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页数：14
相关论文
共 36 条
[1]   Prediction of brake friction materials recovery performance using artificial neural networks [J].
Aleksendric, Dragan ;
Barton, David C. ;
Vasic, Branko .
TRIBOLOGY INTERNATIONAL, 2010, 43 (11) :2092-2099
[2]   Neural network prediction of brake friction materials wear [J].
Aleksendric, Dragan .
WEAR, 2010, 268 (1-2) :117-125
[3]   Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems [J].
Bai, Bin ;
Zhang, Junyi ;
Wu, Xuan ;
Zhu, Guang Wei ;
Li, Xinye .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 177
[4]   Failure analysis of bolted joints Effect of friction coefficients in torque-preloading relationship [J].
Croccolo, D. ;
De Agostinis, M. ;
Vincenzi, N. .
ENGINEERING FAILURE ANALYSIS, 2011, 18 (01) :364-373
[5]   Tribological properties of bolts depending on different screw coatings and lubrications: An experimental study [J].
Croccolo, Dario ;
De Agostinis, Massimiliano ;
Fini, Stefano ;
Olmi, Giorgio .
TRIBOLOGY INTERNATIONAL, 2017, 107 :199-205
[6]   Influence of tightening procedures and lubrication conditions on titanium screw joints for lightweight applications [J].
Croccolo, Dario ;
De Agostinis, Massimiliano ;
Vincenzi, Nicolo .
TRIBOLOGY INTERNATIONAL, 2012, 55 :68-76
[7]   Prediction of friction coefficient on workpiece/fixture contact surface using artificial neural networks [J].
Deiab, I. M. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2010, 224 (B7) :1085-1094
[8]   An optimizing BP neural network algorithm based on genetic algorithm [J].
Ding, Shifei ;
Su, Chunyang ;
Yu, Junzhao .
ARTIFICIAL INTELLIGENCE REVIEW, 2011, 36 (02) :153-162
[9]   Influence of Structure, Roughness, Microhardness, and Friction Coefficient of Zinc Coating on the Tightening Torque of a Threaded Connection [J].
Efremov, V. V. ;
Bondareva, O. S. ;
Dobychina, O. S. ;
Pilla, C. K. .
JOURNAL OF FRICTION AND WEAR, 2023, 44 (05) :304-308
[10]   Mechanical behaviors of bolted joint during tightening using torque control [J].
Fukuoka, T ;
Takaki, T .
JSME INTERNATIONAL JOURNAL SERIES A-SOLID MECHANICS AND MATERIAL ENGINEERING, 1998, 41 (02) :185-191
1234