刀具磨损监测设计与研究

Design and study of tool wear monitoring

为获取变切削条件下刀具磨损量 ,对刀具磨损补偿值进行了分析研究 .在合理选择人工神经网络模型的基础上 ,建立了切削条件下铣削磨损监控系统 .依据机床相关切削参数 ,运用了人工神经网络的方法对铣削数据进行处理 ,以实验方法研究了高速钢立铣刀后刀面磨损BP网络对铣刀的磨损量预报 .实验表明 :该模型适用于变切削条件下的铣刀磨损监控 ,可以较准确地监控铣刀的剧烈磨损 .

During automatic manufacturing processes, such as CIMS and FMS, tool wear is the key monitoring to process stability. A lot of analysis and studies have been done for compensation purpose. A milling cutter wear monitoring system has been established by selecting artificial neural network to forecast the wear of high-speed steel cutters back face by BP network. The experiments show that this model is applicable to monitoring the intense wear of milling cutter.