涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。

Surface Roughness Modeling and Predicting of Coated Cutting ToolWhile High-Speed Milling Mould Steel SKD11

To effectively control and predict the surface quality and efficiency of high hardness mould steel processing,high-speed milling of TiSiN coating tool to mould steel SKD11(62 HRC) is studied in different cutting parameter combinations(spindle speed,feed speed,depth of axial cut and radial depth of cut) and cooling lubrication conditions through the design of orthogonal cutting experiment.Based on BP neural network principle,the surface roughness prediction model is set up,and test is done to verify its accuracy.The research shows:under different cutting conditions,the surface roughness model of TiSiN coating tool on high-speed milling steel SKD11 has better prediction precision,the prediction error is between 3.45-6.25%.It's useful for the mould manufacturing enterprises to select process parameters,control the processing quality and reduce the manufacturing cost.