基于深度学习的TC32钛合金BTA深孔钻削容屑系数和切屑形态研究
Research on gullet-to-chip area ratio and chip morphology of TC32 titanium alloy BTA deep hole drilling based on deep learning
冯亚洲 1陶觅辰 1刘战锋 2孔浩1
作者信息
- 1. 西安石油大学机械工程学院,陕西 西安 710065
- 2. 陕西深孔智越科技有限公司,陕西 西安 710077
- 折叠
摘要
在钛合金深孔钻削过程中,由于其难加工性经常会存在刀具磨损严重、排屑困难和内孔表面质量差等问题.为了获得具有良好内孔表面质量和切屑形态的钛合金深孔类零件,以新型钛合金TC32 为研究对象,在不同工艺参数下基于深度学习和BP神经网络进行了TC32 钛合金的容屑系数预测和加工试验验证.研究结果表明:预测模型的决定系数R2为 0.921,拟合程度和精度较高,预测性能良好;当进给量为 0.08 mm/r、主轴转速为 435 r/min时容屑系数为 5.6,切屑形态以C形屑和短带状屑为主,排屑顺畅且加工过程稳定.
Abstract
In the process of deep hole drilling of titanium alloy,due to its difficulty in processing,there are often problems such as serious tool wear,difficult chip removal and poor surface quality of the inner hole.In order to obtain titanium alloy deep hole parts with good bore surface quality and chip morphology,the new titanium alloy TC32 was taken as the research object,and the gullet-to-chip area ratio prediction and processing test verification of TC32 titanium alloy were carried out based on deep learning and BP neural network under different process parameters.The results indicate that the determination coefficient of the prediction model is 0.921,with a high degree of fitting and accuracy,and good prediction performance.When the feed rate is 0.08 mm/r and the spindle speed is 435 r/min,the gullet-to-chip area ratio is 5.6,The chip morphology is dominated by C-shaped chips and short strip chips,the chip removal smooth and the machining process stable.
关键词
深度学习/BP神经网络/BTA深孔钻削/TC32/钛合金/容屑系数Key words
deep learning/BP neural network/BTA deep hole drilling/TC32 titanium alloy/gullet-to-chip area ratio引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据