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基于神经网络的采煤机截割部可靠性研究 被引量:7

BASED ON NEURAL NETWORK RELIABILITY STUDY OF SHEARER'S CUTTING PART
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摘要 滚筒是采煤机截煤时的主要工作机构,其结构参数及运动参数会直接影响采煤机的工作效率及工作可靠性。基于虚拟样机技术建立了采煤机刚柔耦合模型,通过动力学仿真获得采煤机关键零件等效应力值;仿真不同滚筒转速、牵引速度、滚筒螺旋升角及截线距下截割部摇臂壳体及行星架的等效应力值,获得了滚筒结构、运动参数对采煤机截割部关键零件可靠性的影响趋势;结合神经网络技术,以不同滚筒结构、运动参数下采煤机关键零件的等效应力值作为神经网络训练样本,对螺旋升角优化设计,获得在关键零件应力值最小时的滚筒螺旋升角。该研究为滚筒结构、运动参数的选取提供更为准确的理论依据,具有一定的工程应用价值。 Roller is an important task of the coal winning machine cutting coal institutions,its structure and motion parameters will directly affect the working efficiency and working reliability of coal winning machine.Based on virtual prototype technology coal winning machine the coupled model is established,through dynamic simulation of coal winning machine equivalent stress values of key parts;Simulation different drum rotating speed,drawing speed,cylinder helix Angle,and the cutting line spacing they cut the shell and the equivalent stress value of planet carrier,the roller structure and motion parameters on reliability of key parts of coal winning machine cutting part influence trend;Combined with neural network technology,with different roller structure and motion parameters of the equivalent stress of key parts of coal winning machine values as the neural network training sample,the helix Angle of optimization design,stress value of key parts in the hour of cylinder helix Angle.The research for the drum more accurate theoretical foundation for the selection of structure and motion parameters,has certain engineering application value.
作者 赵丽娟 范佳艺 ZHAO LiJuan;FAN JiaYi(College of Mechanical Engineering,Liaoning Technical University,Fuxin 123000,China)
出处 《机械强度》 CAS CSCD 北大核心 2018年第4期869-874,共6页 Journal of Mechanical Strength
基金 国家自然科学基金项目(51304105 51674134) 中国煤炭工业科技计划项目(MTKJ2009-264)资助~~
关键词 采煤机 滚筒 可靠性 神经网络 优化设计 Shearer Drum Reliability Neural network Optimization design
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