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为实现采煤机记忆截割过程中截割轨迹的实时动态调节,采用理论分析与实验的方法,分析了采煤机滚筒截割过程中的受力情况,应用最小模糊度方法建立滚筒x、y、z轴受力的隶属度函数,根据采集到的截割力信号判定截齿的实时截割环境,采用模糊神经网络控制方法对传统的记忆截割系统进行优化,并通过实验验证系统的精确性与可靠性.研究结果表明:改进后的系统能优化采煤机的记忆截割路径,大大降低截齿的磨损,延长了滚筒的整体使用寿命,提高了综采工作面的生产效率.
In order to realize the real-time dynamic adjustment of the cutting trajectory in the process of memory cutting, the theoretical analysis and experimental methods were used to analyze the stress conditions in the shearer drum cutting process. The minimum ambiguity method was used to establish the drum x, y, z axis force membership function, according to the collected cutting force signal to determine the cutting real-time cutting environment, using fuzzy neural network control method to optimize the traditional memory cutting system, and through experimental verification of the system Accuracy and reliability.The results show that the improved system can optimize the memory cutting path of the shearer, greatly reduce the wear of the picks, extend the overall service life of the drum and increase the production efficiency of the fully mechanized mining face.