摘要
为了改变因寻找最优振动参数和切削参数而需要在实验中大幅度频繁地改变参数的状况,利用小波函数对一维信号逼近能力较强的特点,提出了一种适用于变参数振动钻削加工过程仿真的小波神经网络模型, 并运用灰色理论中的关联分析法对小波神经网络输入权值进行选取。理论分析和实验表明:应用该仿真模型,能在振动钻削过程的不同区段寻找最优的振动参数和切削参数。
To avoid changing parameters largely and frequently in the experiment for seeking optimum vibration parameters and drilling parameters,a wavelet neural network model was proposed for vibration drilling process with varying parameters taking advantage of the characteristic that the wavelet function has better approximation capability to the one-dimension signals, and the input weight was chosen by the grey relevancy analysis. Theoretical analysis and experiment show that the optimum vibration parameters and drilling parameters can be seeked at the different stages of the drilling process by proposed simulation model.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2005年第3期297-300,共4页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金资助项目(59675059)
吉林省科技厅资助项目(20010570)
关键词
计算机应用
小波神经网络模型
振动钻削
仿真
灰色关联分析
computer application
wavelet neural network model
vibration drilling
simulation
grey relevancy analysis
