Tool Wear Prediction When Maching Ti6Al4V Using Different Tool Materials

摘要：

The tool life is different when using different tool materials machine Ti6Al4V under different cutting conditions. Based on the experiments of cutting Ti6Al4V using different tool materials, the tool wear prediction model is established with BP neural network theory. The wear prediction model is verified by further experiments. The results show that there is a valid prediction interval when using BP neural network predicts tool wear. When the cutting speed is in the vicinity of the training sample data, the relative error of the tool wear prediction and experimental results is less than 10％, which can meet the actual production. The tool wear prediction model is helpful for cutting tool material selection, cutting speed optimization and predomination the time of changing tool accurately when cutting titanium alloy.

关键词： Tool material BP neural network Tool wear Wear prediction model

作者: Y. H. Fan M. L. Zheng M. M. Cheng D. Q. Zhang

作者单位: Harbin University of science and technology, Harbin150080, China

会议类型: 国际会议

会议名称: The 4th International Conference on High Speed Machining(第四届高速加工国际会议 ICHSM2010)

会议地点: 武汉

会议语种:英文

页码: 325-329

在线出版日期: 2011-05-28（万方平台首次上网日期，不代表论文的发表时间）