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

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R 2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。

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.