摘要
磨料水射流切割中影响切割深度的因素很多,各工艺参数的选择和合理搭配对切割效果有很大影响,并且难以用精确的数学模型来描述。建立了基于BP神经网络的磨料水射流切割工艺参数智能选择模型,该模型可以映射出切割深度与切割工艺参数之间的关系,并通过多次学习训练达到了较高的精度和较快的收敛速度。
In AWJ cutting,cutting depth is influenced by many factors.Moreover, selection and matching of each processing parameter have great effect on cutting result.It is difficult to build a precise mathematical model of AWJ cutting to describe the rate of effect.So,a model of intelligent selection of processing parameters in AWJ cutting based on BP Artificial Neural Network is applied.It can map the connection between cutting depth and cutting processing parameters.After training and learning carefully,the precision and astringent rate can meet the demand.
出处
《煤矿机械》
北大核心
2007年第6期98-100,共3页
Coal Mine Machinery
关键词
磨料水射流切割
工艺参数
智能选择
BP神经网络
AWJ cutting
processing parameter
intelligent selection
BP artificial neural network
作者简介
阴妍（1979-）,女,江苏徐州人,中国矿业大学机电工程学院教师,从事机械、CAD方面的教学、科研工作,电子信箱： cumtyinyan@126.com.
