随机载荷下截割部输出轴可靠性分析
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摘要
以"MG2×100/455-BWD"新型薄煤层采煤机截割部为工程对象,深入分析输出轴的载荷情况,以应力—强度分布的可靠性干涉理论为基础,考虑载荷作用次数,建立各关键零件故障模式下的动态可靠性模型,计算其可靠度并进行分析。以Matlab编译采煤机载荷样本、Pro/E实体建模、Ansys生成关键零件柔性体,通过专用接口Mechanism/Pro导入Adams建立采煤机截割部的刚柔耦合模型并对其进行动力学仿真,得到不同工况下关键零件的应力信息,以工况参数为神经网络训练样本,将神经网络和虚拟样机技术结合对其他工况下的应力进行预测,减少了虚拟样机的仿真时间和工作量,提高了其动态可靠性,同时为采煤机输出轴设计提供可靠的参数依据。
With the "MG2×100/455-BWD" new type of thin coal seam shearer cutting project as the object, its key parts which include the output shaft, shell, carrier load situation have been depth analyzed. Considering the number of loads, the dynamic reliability model of each key part failure mode which Based on the reliability interference theory of stress-intensity distribution is established to calculate and analyze its reliability. By Compiling miner load samples with MATLAB, building Solid modeling with Pro/E, generating key parts Flexible body with ANSYS, the rigid and flexible coupling model of the shearing part of the shearer was established and the simulation was carried out by using the special interface MECHANISM/Pro to import ADAMS to get the stress information of the key parts under different working conditions. Taking condition parameters as neural network training samples, the neural network and the virtual prototyping technology are combined to predict the stress under other working conditions, which reduces the simulation time and the workload of the virtual prototype. The research in the paper provides the basis for the design of the shearer, improves its dynamic reliability, shortens the product design cycle, has important theoretical significance and high application value.
With the "MG2×100/455-BWD" new type of thin coal seam shearer cutting project as the object, its key parts which include the output shaft, shell, carrier load situation have been depth analyzed. Considering the number of loads, the dynamic reliability model of each key part failure mode which Based on the reliability interference theory of stress-intensity distribution is established to calculate and analyze its reliability. By Compiling miner load samples with MATLAB, building Solid modeling with Pro/E, generating key parts Flexible body with ANSYS, the rigid and flexible coupling model of the shearing part of the shearer was established and the simulation was carried out by using the special interface MECHANISM/Pro to import ADAMS to get the stress information of the key parts under different working conditions. Taking condition parameters as neural network training samples, the neural network and the virtual prototyping technology are combined to predict the stress under other working conditions, which reduces the simulation time and the workload of the virtual prototype. The research in the paper provides the basis for the design of the shearer, improves its dynamic reliability, shortens the product design cycle, has important theoretical significance and high application value.
引文
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[15] 曲振.采煤机截割部扭矩轴的可靠性研究[D].阜新:辽宁工程技术大学,2009:37-43.QU Zhen.Research on reliability of torque shafts in shearer cutting machine [D].Fuxin:Liaoning Technical University,2 009:37-43(In Chinese).
[16] Salsani Armin.Daneshian Jahanbakhsh,Shariati Shahram,ect.Predicting roadheader performance by using artificial neural network [J].Neural Computing and Applications,2013:1—9.
[17] 张伟强,郭金.42CrMo钢在不同温度下单向拉伸的流变特征[J].金属热处理,2012,37(7):94-97.ZHANG WeiQiang,GUO Jin.Rheological characteristics of 42CrMo steel under uniaxial tension at different temperatures [J].Metal Heat Treatment,2012,37 (7):94-97(In Chinese).
[2] 胡登高.范迅.薄煤层开采发展趋势及自动化开采实践[J].矿业装备,2015(11):94-97.HU DengGao.FAN Xun.Development trend of thin coal seam mining and practice of automatic mining [J].Mining Equipment,2015 (11):94-97(In Chinese).
[3] 赵丽娟,马永志.基于多体动力学的采煤机截割部可靠性研究[J].煤炭学报,2009,34(9):1271-1275.ZHAO LiJuan,MA YongZhi.Study on reliability of shearer cutting part based on multi-body dynamics [J].Journal of Coal Science,2009,34 (9):1271-1275(In Chinese).
[4] 赵丽娟,杨程程.薄煤层采煤机截割部可靠性分析[J].制造业自动化,2012,34(1):4-6.ZHAO LiJuan,YANG ChengCheng.Reliability analysis of cutting part of thin coal seam shearer [J].Manufacturing Automation,2012,34 (1):4-6
[5] 赵丽娟,马联伟.薄煤层采煤机可靠性分析与疲劳寿命预测[J].煤炭学报.2013,38(7):1287-1292.ZHAO LiJuan,MA LianWei.Evaluation analysis and fatigue life prediction of thin coal seam shearer [J].Journal of China Coal Society,2013,38 (7):1287-1292(In Chinese).
[6] 张义民,王婷,黄婧.采煤机摇臂系统行星轮系疲劳可靠性灵敏度设计[J].东北大学学报(自然科学版),2016,37(10):1426-1431.ZHANG YiMin,WANG Ting,HUANG Jing.Designability of fatigue reliability sensitivity of planetary gear train for rocker system of coal mining machine [J].Journal of Northeastern University(Natural Science Edition),2016,37 (10):1426-1431(In Chinese).
[7] 谢里阳.机械可靠性基本理论与方法[M].北京:科学出版社,2012:100-217.XIE LiYang.The basic theory and method of mechanical reliability [M].Beijing:Science Press,2012:100-217(In Chinese).
[8] 张义民.机械动态与渐变可靠性理论与技术评述[J].机械工程学报,2013,49(20):101-114.Zhang YiMin.Journal of mechanical engineering,[J].Chinese Journal of Mechanical Engineering,2013,49 (20):101-114(In Chinese).
[9] 谢里阳.机械可靠性理论、方法及模型中若干问题评述[J].机械工程学报,2014,50(14):27-35.XIE LiYang.Journal of Mechanical Engineering [J].Journal of Mechanical Engineering,2014,50 (14):27-35(In Chinese).
[10] Csikszentmihalyi M,Larson R.Validity and reliability of the Experience-Sampling Method.[J].Journal of Nervous & Mental Disease,2014,175(9):526.
[11] 王丹玉,王启佳,孙维.采煤机牵引速度的分析与选择[J].煤矿机械,2012,33(10):201-203.WANG DanJiu,WANG QiJia,SUN Wei.Study and selection of traction speed of shearer [J].Coal Mine Machinery,2012,33 (10):201-203(In Chinese).
[12] Bian H,Liu S,Cai G,et al.Artificial neural network model for predicting soil electrical resistivity[J].Journal of Intelligent & Fuzzy Systems,2015,29(5):1751-1759.
[13] 武玉梁.基于MATLAB人工神经网络预测预报冲击地压的研究[D].青岛:山东科技大学,2005:35-67.WU Yu Liang.Based on MATLAB artificial neural network prediction and prediction of rockburst pressure [D].Shandong University of Science and Technology,2005:35-67(In Chinese).
[14] 崔亚新.基于双神经网络的微孔钻削在线监测研究[D].长春:吉林大学,2012:47-91.CUI YaXin.Study on on-line monitoring of micropore drilling based on dual neural network [D].Changchun:Jilin University,2012:47-91(In Chinese).
[15] 曲振.采煤机截割部扭矩轴的可靠性研究[D].阜新:辽宁工程技术大学,2009:37-43.QU Zhen.Research on reliability of torque shafts in shearer cutting machine [D].Fuxin:Liaoning Technical University,2 009:37-43(In Chinese).
[16] Salsani Armin.Daneshian Jahanbakhsh,Shariati Shahram,ect.Predicting roadheader performance by using artificial neural network [J].Neural Computing and Applications,2013:1—9.
[17] 张伟强,郭金.42CrMo钢在不同温度下单向拉伸的流变特征[J].金属热处理,2012,37(7):94-97.ZHANG WeiQiang,GUO Jin.Rheological characteristics of 42CrMo steel under uniaxial tension at different temperatures [J].Metal Heat Treatment,2012,37 (7):94-97(In Chinese).