一种在线监测铣刀磨损量的新方法

提出了一种在线监测铣刀磨损量的新方法,该方法利用B样条神经网络建立不同刀具磨损状态下加工参数与切削力之间的映射关系。通过比较实时采集的切削力与不同刀具磨损值对应的切削力大小,可确定刀具的磨损状态,并利用建立的简化模型计算刀具的精确磨损值。试验结果表明,该方法消除了加工参数变化对特征的影响,简化了特征选取的方法,能够适应外部加工环境的变化,完全满足刀具状态监测系统的实用化需求。

A New Methodology for On-line Tool Wear Monitoring in Face Milling Operations

A new tool wear monitoring methodology for face milling operations was introduced. The mapping relationship between machining parameters and cutting forces in different tool wear states was built up by means of B-spline neural network in this new methodology model. The accurate tool conditions could be classified through comparing real cutting forces with standard ones in different tool wear, and the precise amount of tool wear could be calculated by using a simplified model. The experimental results show that the methodology can eliminate the effects of various machining parameters, simplify the work of feature extraction, adapt fully the combinations of machining parameters and meet the needs of Tool Wear Monitoring System for engineering applications.