PCD刀具车削超硬铝合金的切削性能研究

2020-10-16

作者：马殿文，沈春根，张宇，姚炀单位：江苏大学

摘要：以Al7075T6为加工对象，通过车削试验对PCD刀具车削超硬铝合金的三向动态切削力和表面粗糙度展开研究，建立基于BP神经网络的切削力和表面粗糙度预测模型。结果表明：随着切削用量三要素的变化，切削力变化显著；对于表面粗糙度而言，背吃刀量、进给量和切削速度之间无交互作用；基于LM优化算法的BP神经网络对样本的拟合度高，且对切削力和表面粗糙度的预测精度高。

关键词：PCD刀具；超硬铝合金；切削力；表面粗糙度；预测模型

中图分类号：TG51；TH161.1文献标志码：ADOI：10.3969/j.issn.1000-7008.2020.03.004

Research on Cutting Performance of PCD Tool for Turning Superhard

Aluminum Alloy

Ma Dianwen，Shen Chungen，Zhang Yu，Yao Yang

Abstract:Al7075T6 is used as the processing object，the threeway dynamic cutting force and surface roughness of PCD tool turning superhard aluminum alloy are studied by turning test.The cutting force and surface roughness prediction model based on BP neural network is established.The results show that the cutting force changes significantly with the change of the three factors of cutting amount.For the surface roughness，there is basically no interaction between cutting depth，the feed rate and the cutting speed；BP based on LM optimization algorithm the neural network has a very good fit to the sample，the prediction accuracy of the cutting force is very high，the prediction accuracy of the surface roughness is high.

Keywords:PCD tool；superhard aluminum alloy；cutting force；surface roughness；prediction model