木塑复合材料铣削加工表面粗糙度及其预测模型的研究

Research on Milling Surface Roughness of Wood-Plastic Composites and Its Prediction Model

在木塑复合材料(WPC)的加工过程中,加工精度低、切削表面质量差等问题时有发生,而切削力和切削温度对刀具的寿命和加工表面质量有着非常重要的影响。采用硬质合金单齿柄铣刀对木塑复合材料进行铣削试验,研究主轴转速、进给速度、切削深度等切削参数对切削力、切削温度、加工表面粗糙度的影响。在试验数据的基础上,采用BP神经网络建立了WPC加工表面粗糙度的预测模型。结果表明,随着主轴转速增大,WPC的切削力减小,切削温度增大,表面粗糙度减小;随着进给速度增大,WPC的切削力增大,切削温度减小,表面粗糙度增大;随着铣削深度增加,WPC的切削力增大,切削温度增大,表面粗糙度逐渐增大。建立的预测模型具有较高的精度,能够用于WPC铣削加工表面粗糙度的预测,为提高WPC加工表面质量、刀具使用寿命提供了理论和实践指导。

Low machining accuracy,poor cutting surface quality and other problems often occur during the processing of wood-plastic composites(WPC),and cutting force and cutting temperature impose important impacts on tool life and surface quality.In this paper,a cemented carbide single tooth handle milling cutter was used for milling tests on WPC to study the effects of cutting parameters such as spindle speed,feed rate and cutting depth on cutting force,cutting temperature and machined surface roughness.Based on the experimental data,BP neural network was adopted to establish a prediction model for the surface roughness of WPC machining.The results showed that along with the increase of spindle speed,the cutting temperature increased,but the cutting force and surface roughness decreased.As the feed speed increased,the cutting temperature decreased,but the cutting force and surface roughness increased.With the increase of milling depth,the cutting force,cutting temperature and surface roughness increased.The prediction model has high accuracy and can be used to predict the surface roughness in WPC milling.It provides theoretical and practical guidance for improving WPC machining surface quality and tool service life.