摘要
通过正交实验设计方法,开展了300M钢的铣削加工研究,对各种工况下加工表面的粗糙度进行了测量。运用极差分析方法,阐明了主轴转速、每齿进给量、轴向切深及径向切宽对表面粗糙度的影响程度和影响规律,并采用BP神经网络建立了表面粗糙度的指数预测模型。实验及分析结果显示,每齿进给量是影响300M钢逆铣表面粗糙度的主要因素,顺铣时主要影响因素则为轴向切深。同时,通过对比预测值和实验值,验证了表面粗糙度预测模型构建的合理性和有效性。
To evaluate the infl uence of cutting parameters on the surface roughness of 300 M stainless steel, research on milling process is conducted by orthogonal experiment design method. The influence degree of spindle speed, feed rate, radial cutting depth and axial cutting depth on surface roughness is respectively illuminated using range analysis method. Besides, BP neural network is applied to construct the prediction model for surface roughness. The analysis result shows that the main infl uence factors on surface roughness is feed rate in the condition of up milling, axial cutting depth in the condition of down milling. Meantime, by comparing between predictive value and experimental value, the rationality and effectiveness of the prediction model is verifi ed.
出处
《航空制造技术》
2015年第S1期96-99,共4页
Aeronautical Manufacturing Technology
关键词
300M钢
高速铣削
表面粗糙度
极差分析
BP神经网络模型
300M stainless steel
High-speed milling
Surface roughness
Range analysis
BP neural
network model
