高速铣削工件表面粗糙度的预测  

Surface Roughness Prediction in High-Speed Milling

作　　者：王义强[1,2] 闫国琛 王晓军[1,2] 邓猛[1] 

机构地区：[1]浙江大学宁波理工学院机电与能源工程学院,浙江宁波315100 [2]浙江省零件轧制成形技术研究重点实验室,浙江宁波315100

出　　处：《机械设计与制造》2014年第11期131-133,137,共4页Machinery Design & Manufacture

基　　金：浙江省自然科学基金(Y1110708);国家科技重大专项(2012ZX04011021);宁波市自然科学基金(2013A610152)

摘　　要：表面粗糙度是衡量工件表面质量的重要指标。采用正交试验方法,利用圆环面铣刀对模具钢NAK80进行了高速铣削试验,测量了不同工艺参数下的工件表面粗糙度。将试验结果与人工智能中的BP神经网络结合,建立了表面粗糙度预测模型,用于预测不同主轴转速、进给速度、切削深度、切削行距、刀具倾角时被加工工件的表面粗糙度,并通过MATLAB图形用户界面设计了表面粗糙度预测软件。结果表明,该预测模型及其封装后的软件可用于加工前工件表面粗糙度的预测。Surface roughness is an important index to measure the quality of the workpiece surface. With the method of the orthogonal experiment, high-speed milling experiment for NAKS0 die steel is conducted with toroidal cutter and surface roughness is measured. Surface roughness prediction model is established based on the experimental results and BP artificial neural network. Surface roughness can be predicted under different process parameters such as spindle speed, feed rate, axial depth of cut, radial depth of cut and tool angle. Furthermore, surface roughness prediction software is designed through the MATLAB GUt Results show that the prediction model and its software package can be used to prediction of surface roughness before processing.

关 键 词：高速铣削 表面粗糙度 预测 圆环面铣刀 人工神经网络 

分 类 号：TH16[机械工程—机械制造及自动化]