Study on fatigue life prediction and influencing factors of roadheader rotary platform

doi:10.3785/j.issn.1006-754X.2025.04.118

Chinese Journal of Engineering Design

2025, Vol. 32

Issue (1): 92-101 DOI: 10.3785/j.issn.1006-754X.2025.04.118

Reliability and Quality Design

Study on fatigue life prediction and influencing factors of roadheader rotary platform

Liyong TIAN(

),Jiahao ZHANG(

),Ning YU,Xiaohan YU,Shuo ZHANG

School of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China

Download:

HTML

PDF(5358KB)
Export: BibTeX | EndNote (RIS)

Abstract

The rotary platform of the roadheader bears eccentric load and strong impact when cutting coal and rock, and its performance affects the working efficiency and safety of the roadheader. To explore the influencing factors of fatigue life of the rotary platform and identify the optimal service parameters of the roadheader, a fatigue life prediction method for the rotary platform based on the Kriging surrogate model and DEM-MFBD (discrete element method-multi flexible body dynamics) bidirectional coupling technology was proposed. Firstly, the spatial force models for the cutting part and rotary platform of the roadheader were established, and the force law of the cutting part and rotary platform was clarified. Then, the bidirectional rigid-flexible coupling dynamics simulation analysis for the rotary platform was conducted by combining RecurDyn and EDEM software to obtain the stress distribution of the rotary platform under the working condition. Finally, 15 groups of service parameters of roadheader were selected by Latin hypercube sampling method as input, and the corresponding Kriging surrogate model was established with the fatigue life of the rotary platform as the response. The surrogate model was optimized by particle swarm optimization algorithm to obtain the fatigue life of the rotary platform under the optimal service parameters. The results showed that the fatigue life of the rotary platform was maximum when the cutting head speed of the roadheader was 54 r/min, the lateral swing speed of rotary platform was 1.003 m/min, and the vertical swing angle of cutting arm was 7°. Combining DEM-MFBD bidirectional coupling technology, Kriging surrogate model and particle swarm optimization algorithm to explore the optimal service parameters of the roadheader can provide new ideas for the optimization design of rotary components.

Key words： rotary platform DEM-MFBD bidirectional coupling technology fatigue life prediction Kriging surrogate model particle swarm optimization algorithm

Received: 29 February 2024 Published: 04 March 2025

CLC:

TD 421

Corresponding Authors: Jiahao ZHANG E-mail: tianliyong2003@163.com;1452565886@qq.com

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liyong TIAN
	Jiahao ZHANG
	Ning YU
	Xiaohan YU
	Shuo ZHANG

Cite this article:

Liyong TIAN,Jiahao ZHANG,Ning YU,Xiaohan YU,Shuo ZHANG. Study on fatigue life prediction and influencing factors of roadheader rotary platform. Chinese Journal of Engineering Design, 2025, 32(1): 92-101.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2025.04.118 OR https://www.zjujournals.com/gcsjxb/Y2025/V32/I1/92

掘进机回转台疲劳寿命预测及影响因素研究

掘进机回转台在截割煤岩时承受偏载荷及强冲击作用，其性能影响掘进机的工作效率及安全性。为探究掘进机回转台疲劳寿命的影响因素及最佳服役参数，提出了一种基于Kriging代理模型和DEM-MFBD（discrete element model-multi flexible body dynamics，离散单元法-多柔性体动力学）双向耦合技术的回转台疲劳寿命预测方法。首先，建立了掘进机截割部与回转台的空间受力模型，明确了截割部与回转台的受力规律。然后，联合RecurDyn与EDEM软件对回转台进行双向刚柔耦合动力学仿真分析，获得了回转台在工作状态下的应力分布。最后，利用拉丁超立方抽样法选取15组掘进机服役参数作为输入，以回转台疲劳寿命为响应，建立了对应的Kriging代理模型，并利用粒子群优化算法对代理模型进行寻优，得到了回转台在最佳服役参数下的疲劳寿命。结果表明，当掘进机的截割头转速为54 r/min、回转台横摆速度为1.003 m/min、截割臂垂直摆角为7°时，回转台的疲劳寿命最长。结合DEM-MFBD双向耦合技术、Kriging代理模型与粒子群优化算法来探究掘进机的最佳服役参数，可为回转类部件的优化设计提供新思路。

关键词： 回转台, DEM-MFBD双向耦合技术, 疲劳寿命预测, Kriging代理模型, 粒子群优化算法

Fig.1 Structure of cutting part and rotary platform of roadheader

Fig.2 Mechanical model of cutting part and rotary platform of roadheader

Fig.3 Mechanical model of cutting part

Fig.4 Mechanical model of rotary platform

Fig.5 Rigid-flexible coupling models of roadheader under different service conditions

参数	数值
泊松比	0.21
剪切模量/MPa	18 300
单位面积法向刚度/(N/m³)	1.067 8 $×$ 10⁹
单位面积切向刚度/(N/m³)	8.542 5 $×$ 10⁸
最大法向应力/Pa	2.817 9 $×$ 10⁷
最大切向应力/Pa	1.259 2 $×$ 10⁷

Table 1 Parameters of coal samples

Fig.6 Cloud map of maximum equivalent stress of rotary platform

Fig.7 Variation curve of equivalent stress at the node with the maximum force on rotary platform

Table 2 Material parameters of Q235 steel

Fig.8 S-N curve of Q235 steel

Fig.9 Fatigue life cloud map of rotary platform under working condition A

Table 3 Value range of design variables

Fig.10 Latin hypercube sampling results of service parameters of roadheader

组别	服役参数			最大应力循环数N/次
组别	n/(r/min)	v/(m/min)	ω/(°)	最大应力循环数N/次
1	54.4	1.48	41.3	1.50 $×$ 10⁵
2	47.4	1.45	0.5	9.54 $×$ 10⁴
3	40.6	1.48	-17.5	2.17 $×$ 10⁴
4	43.7	1.32	20.6	1.20 $×$ 10⁶
5	43.9	1.17	9.0	4.58 $×$ 10⁶
6	51.8	1.47	31.9	2.93 $×$ 10⁵
7	54.1	1.36	7.0	3.90 $×$ 10⁶
8	48.0	1.21	-3.4	4.60 $×$ 10⁶
9	50.8	1.42	-7.9	2.19 $×$ 10⁶
10	36.2	1.36	37.9	1.28 $×$ 10⁵
11	52.6	1.41	24.1	5.63 $×$ 10⁵
12	42.5	1.33	16.7	7.29 $×$ 10⁵
13	36.6	1.26	-13.9	2.44 $×$ 10⁶
14	40.2	1.00	29.9	4.88 $×$ 10⁶
15	42.6	1.21	-2.9	3.39 $×$ 10⁶

Table 4 Sampling scheme of roadheader service parameters and fatigue life simulation results of rotary platform

Table 5 Fitting accuracy of Kriging surrogate model

Table 6 Comparison between simulated and predicted fatigue life of rotary platform

Fig.11 Response surface of influence of roadheader service parameters on fatigue life of rotary platform

Fig.12 Optimization process of service parameters of roadheader based on PSO algorithm

Fig.13 Iterative curve for fatigue life optimization of rotary platform based on PSO algorithm


[1]	董磊，任家骏，王喜胜，等. 悬臂式掘进机回转机构的建模与动力学仿真［J］. 煤炭科学技术， 2009， 37（6）： 83-85. DONG L， REN J J， WANG X S， et al. Model establishing and dynamics simulation of rotary mechanism for boom type roadheader［J］. Coal Science and Technology， 2009， 37（6）： 83-85.

[2]	赵丽娟，刘旭南，曹拓. 纵轴式掘进机横摆运动参数的优化设计［J］. 煤炭学报， 2012， 37（12）： 2112-2117. ZHAO L J， LIU X N， CAO T. Horizontal swing movement parameters optimization design of longitudinal road header［J］. Journal of China Coal Society， 2012， 37（12）： 2112-2117.

[3]	赵丽娟，李明昊，张品好. 掘进机回转台动态可靠性与疲劳寿命分析［J］. 机械强度， 2017， 39（4）： 887-892. ZHAO L J， LI M H， ZHANG P H. Dynamic reliability and fatigue life analysis of the roadheader's gyration platform［J］. Journal of Mechanical Strength， 2017， 39（4）： 887-892.

[4]	张路伟. 基于整机振动的掘进机回转台疲劳寿命分析及优化设计［D］. 太原：太原理工大学， 2021： 5-6. ZHANG L W. Fatigue life analysis and optimization design of roadheader turret based on whole machine vibration［D］. Taiyuan： Taiyuan University of Technology， 2021： 5-6.

[5]	王岩. 掘进机截割过程建模与仿真研究［D］. 阜新：辽宁工程技术大学， 2021： 4-5. WANG Y. Research on modeling and simulation of the process of the roadheader cutting［D］. Fuxin： Liaoning Technical University， 2021： 4-5.

[6]	周开平. 煤矿井下悬臂式掘进机回转机构优化设计［J］. 煤炭技术， 2021， 40（9）： 177-180. ZHOU K P. Optimization design of slewing mechanism of cantilever roadheader in coal mine［J］. Coal Technology， 2021， 40（9）： 177-180.

[7]	刘德. EBZ-300型纵轴式掘进机回转机构优化设计的研究［D］. 太原：太原理工大学， 2018： 4-5. doi：10.35530/it.068.04.1290 LIU D. Study on optimal design of rotary mechanism of EBZ-300 longitudinal roadheader［D］. Taiyuan： Taiyuan University of Technology， 2018： 4-5. doi: 10.35530/it.068.04.1290

[8]	商跃进. 悬臂式掘进机回转机构虚拟样机优化设计［J］. 中国工程机械学报， 2015， 13（2）： 135-138. SHANG Y J. Virtual prototyping and optimization design on rotary mechanism of boom-type roadheaders［J］. Chinese Journal of Construction Machinery， 2015， 13（2）： 135-138.

[9]	李旭，顾永正，吴淼. 基于微分几何的掘进机工作机构运动学分析［J］. 煤炭学报， 2016， 41（12）： 3158-3166. LI X， GU Y Z， WU M. Kinematics analysis of roadheader's working mechanism based on differential geometry［J］. Journal of China Coal Society， 2016， 41（12）： 3158-3166.

[10]	张振山，王建军，武秋俊，等. 基于ANSYS的矿用掘进机回转台振动疲劳分析方法［J］. 煤炭技术， 2023， 42（6）： 248-250. ZHANG Z S， WANG J J， WU Q J， et al. Vibration fatigue analysis method of rotary table of mining tunneling machine based on ANSYS［J］. Coal Technology， 2023， 42（6）： 248-250.

[11]	金秀宇，胡建华，郭玲. 掘进机回转台可靠性分析研究［J］. 煤矿机械， 2022， 43（2）： 41-43. JIN X Y， HU J H， GUO L. Study on reliability analysis of rotary platform of roadheader［J］. Coal Mine Machinery， 2022， 43（2）： 41-43.

[12]	刘丽娟. EBZ-132型纵轴式掘进机虚拟样机建模与动力学分析［D］. 武汉：中国地质大学， 2013： 44-45. doi：10.4028/www.scientific.net/amm.278-280.295 LIU L J. Virtual prototype modeling and dynamics analysis of EBZ-132-type longitudinal roadheader［D］. Wuhan： China University of Geosciences， 2013： 44-45. doi: 10.4028/www.scientific.net/amm.278-280.295

[13]	田立勇. 基于多源数据融合的采煤机截割载荷识别与预测研究［D］. 阜新：辽宁工程技术大学， 2020： 12-21. TIAN L Y. Research on identification and prediction of shearer cutting load based on multi-source data fusion［D］. Fuxin： Liaoning Technical University， 2020： 12-21.

[14]	曹均. 悬臂式掘进机截割部虚拟样机建模及仿真［D］. 太原：太原理工大学， 2014： 26-28. CAO J. Virtual prototype modeling and simulation of roadheader cutting unit［D］. Taiyuan： Taiyuan University of Technology， 2014： 26-28.

[15]	付万里. 基于改进试验选点的直接GEK可靠度代理模型方法［D］. 哈尔滨：哈尔滨工业大学， 2018： 9-10. FU W L. The direct GEK surrogate model method based on improved experimental selection［D］. Harbin： Harbin Institute of Technology， 2018： 9-10.

[16]	卢高明，李元辉，张希巍，等. 周期荷载作用下黄砂岩疲劳破坏变形特性试验研究［J］. 岩土工程学报， 2015， 37（10）： 1886-1892. LU G M， LI Y H， ZHANG X W， et al. Fatigue deformation characteristics of yellow sandstone under cyclic loading［J］. Chinese Journal of Geotechnical Engineering， 2015， 37（10）： 1886-1892.

[17]	胡楷，马军，王晓东，等. 基于Kriging组合模型和NSGA-Ⅲ算法的转子裂纹参数识别［J］. 动力工程学报， 2023， 43（11）： 1506-1514. HU K， MA J， WANG X D， et al. Rotor crack parameter identification based on Kriging combined model and NSGA-Ⅲ algorithm［J］. Journal of Chinese Society of Power Engineering， 2023， 43（11）： 1506-1514.

[18]	姜楠. 高速飞行器概念设计软件平台研制［D］. 南京：南京航空航天大学， 2020： 29-30. JIANG N. Development of high-speed aircraft conceptual design software platform［D］. Nanjing： Nanjing University of Aeronautics and Astronautics， 2020： 29-30.

[19]	蒋先酒. 基于Kriging的车载身管武器高低机末级齿轮可靠性研究［D］. 沈阳：沈阳理工大学， 2023： 14-17. JIANG X J. Research on reliability of final gear of elevating machine of vehicle barrel weapon based on Kriging［D］. Shenyang： Shenyang Ligong University， 2023： 14-17.

[20]	刘英杰. 基于数字型超构波导的硅基多维复用器件研究［D］. 哈尔滨：哈尔滨工业大学， 2022： 35-36. LIU Y J. Silicon-based multiplexing devices with digital meta-structure［D］. Harbin： Harbin Institute of Technology， 2022： 35-36.

[1]	Hao LI,Ying WANG,Yaoshuai MA,Chunya SUN,rongjie HUANG,Haoqi WANG,Linli LI. Research on structural optimization design for powder separator of large vertical mill based on Kriging model[J]. Chinese Journal of Engineering Design, 2024, 31(6): 801-809.

[2]	Bofu WU,Yaoye WU,Jing BEI,Zongyang WU,Liang SUN. Multi-objective reliability optimization design for cast aluminum integrated car door[J]. Chinese Journal of Engineering Design, 2024, 31(2): 188-200.

[3]	SUN Yue-hai, TANG Er-xing. Optimal design of basic parameters of spiral bevel gears[J]. Chinese Journal of Engineering Design, 2020, 27(5): 616-624.

[4]	WANG Ai-lun, LIU Le, LIU Qing-ya. Research on strength reliability of pull rod combined rotor based on Kriging surrogate model[J]. Chinese Journal of Engineering Design, 2019, 26(4): 433-440.

[5]	ZHAO Dong-bo, YAO Ling-ling, YUAN Kun-kun, LU Jin-gui. Multi-objective optimization for four-bar mechanism of hydraulic support based on particle swarm optimization algorithm[J]. Chinese Journal of Engineering Design, 2017, 24(4): 433-439.

[6]	HUANG Xiao-qian, WANG Feng, TAN Yang-hong, WANG Rui, SHAO Jing-ke, CHEN Chun. Coordinated scheduling of electric vehicles and renewable generation considering vehicle-to-grid mode[J]. Chinese Journal of Engineering Design, 2016, 23(1): 67-73.

[7]	SHEN Jie-bin, TANG Dong-lin. Study of field size parameter in stress field intensity approach[J]. Chinese Journal of Engineering Design, 2016, 23(1): 22-27.

[8]	LIU Zhe, TAO Feng-he, JIA Chang-zhi. Fatigue life prediction on drive shaft of tracked vehicle and structure optimization[J]. Chinese Journal of Engineering Design, 2015, 22(5): 431-437.

[9]	WANG Xin-Gang, ZHANG Yi-Min, WANG Bao-Yan. Research on reliability-based dynamic fatigue of locomotive wheels[J]. Chinese Journal of Engineering Design, 2009, 16(4): 247-251.

[10]	GAO Jing,SONG Jian,ZHANG Bu-Liang,FENG Xi-Cheng. Prediction on fatigue life of vehicle's driving axle house based on MSC.Fatigue[J]. Chinese Journal of Engineering Design, 2007, 14(3): 210-214.

Viewed

Full text

Abstract

Cited

Shared

Discussed