基于BP神经网络算法的锚钻机进给装置精密控制系统研究

Ning Zhang
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引用次数: 0

摘要

根据煤矿井下巷道围岩支护的施工要求,设计研制了一种智能锚固装置。通过仿真设计详细介绍了送料装置的结构,分析了精密控制系统的工作原理。通过BP神经网络算法对精度控制系统进行研究,得到进给装置各部件的施工步骤,根据巷道施工图像信息和钻孔施工数据,独立判断施工方位和角度,无需人工干预。为了保证给料装置的施工效率,采用BP神经网络算法对各施工步骤进行优化,得到最优施工步骤。整体计算效率提高16.67%。该系统能够保证送料装置的独立切换,能够独立完成钻井施工、锚固剂输送、锚杆安装的全过程。该装置适用范围广,作业效率高,为煤矿井下锚杆钻机的智能化施工提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Precise Control System of Anchor Drilling Rig Feeding Device Based on BP Neural Network Algorithm
According to the construction requirements of the surrounding rock support of underground roadway in coal mine, an intelligent anchoring unit is designed and developed. The structure of the feeding device is introduced in detail through the simulation design, and the working principle of precision control system is analyzed. The precision control system is studied through BP neural network algorithm, and the construction steps of each component of the feed device are obtained, According to the roadway construction image information and the drilling construction data, the construction orientation and angle are judged independently without manual intervention. In order to ensure the construction efficiency of the feeding device, the BP neural network algorithm is used to optimize each construction step and obtain the optimal construction step. The overall calculation efficiency is increased by 16.67%. The system can ensure the independent switching of the feeding device, and can independently complete the whole process of drilling construction, anchor agent transportation and anchor bolt installation. The device has wide application range and high operation efficiency, which provides a theoretical basis for the intelligent construction of underground anchor drill in coal mine.
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