摘要
为对采煤机截割部齿轮进行创新设计与研究,建立了采煤机摇臂系统的刚柔耦合虚拟样机模型,研究其齿轮系统热平衡过程,确定温度载荷和边界条件,加载动力学仿真软件Adams输出的不同工况的载荷文件。应用有限元软件Ansys对齿轮进行了温度-结构耦合分析,得到了齿轮的温度场及结构场云图。将多场耦合(MFC)与神经网络(ANNs)技术结合,即采用MFC-ANNs技术,可以预测齿轮可靠工作时采煤机的运动学参数,误差仅为5.653 8×10^(-6),为齿轮类零件的设计与优化提供了明确的量化依据,可有效提高该类零件工作的可靠性,对采煤机实际生产具有指导意义。
In order to do innovative design and study for the gears of shearer＇ s cutting part, the rigid-flexible coupling model of rocker system was established to study the thermal equilibrium procedure of the gear system, the temperature load and boundary conditions were determined and the different working conditions load files which were output by MSCAdams were loaded. Temperature-structure coupling analysis of the gears were studied by ANSYS, the temperature field nephogram and the structure coupling analysis nephogram of the gears were obtained. Combining MFC with ANNs could predict shearer＇ s kinematics parameters when the gears was reliable operation, the prediction error reach 5. 6538×10-6, the research results provides a unequivocal quantitative basis for the design and optimization of gear type parts which could improve this type of parts＇ functional reliability effectively, this method has instruction significance to practical shearer＇ s productions.
出处
《机械强度》
CAS
CSCD
北大核心
2016年第6期1264-1270,共7页
Journal of Mechanical Strength
基金
国家科技支撑项目(2007BAF12B01)
中国煤炭工业科技计划项目(MTKJ2009-264)资助~~
关键词
采煤机
截割部
齿轮
多场耦合
神经网络
Shearer
Cutting part
Gear
Multi-field coupling
Artificial neural network
