RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS

ZHAO LiJuan; LI MingHao

doi:10.16579/j.issn.1001.9669.2016.06.023

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RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS

更新时间：2022-09-22

- RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS
- Journal of Mechanical Strength Vol. 38, Issue 6, Pages: 1264-1270(2016)
- 作者机构：
  
  1. 辽宁工程技术大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2016.06.023
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ZHAO LiJuan, LI MingHao. RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS. [J]. 38(6):1264-1270(2016)
DOI：

ZHAO LiJuan, LI MingHao. RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS. [J]. 38(6):1264-1270(2016) DOI： 10.16579/j.issn.1001.9669.2016.06.023.

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。

Abstract

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.

关键词

采煤机截割部齿轮多场耦合神经网络

Keywords

ShearerCutting partGearMulti-field couplingArtificial neural network

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