一种立铣刀热变形在线状态识别方法研究

Study on On-line Recognition Method for Thermal Deformation State of End Mill

机床在铣削加工时总伴随着大量的切削热产生,为了减少热变形对刀具加工精度的影响,本文以硬质合金立铣刀作为研究对象,提出一种基于BP神经网络的立铣刀在铣削过程中热变形实时状态的识别方法。搭建测试平台进行立铣刀热误差实验,并设计了一种立铣刀热变形变形量的直接测量法。采集机床连续加工期间主轴的温度信号和立铣刀热误差变形量,对温度信号进行特征提取。将刀具不同热变形状态及相关的特征值,输入到8-4-2的三层BP神经网络模型进行训练。实验结果表明:刀具热变形识别系统识别率在为87.2%左右。

In order to reduce the influence of the thermal deformation on the machining accuracy of the cutter, taking the carbide end milling cutter as the research object, a method based on BP neural network to identify the real-time thermal error state of the end milling cutter in the milling is proposed. A test platform was built to carry out the thermal error experiment of end milling cutter, and a direct measurement method of thermal error deformation of end milling cutter was designed to collect the temperature signal of spindle during continuous addition of machine tool and thermal error deformation of end milling cutter. The different thermal deformation states of the cutter and their associated eigenvalues were input into the 8-4-2 three-layer BP neural network model. The experimental results show that the recognition rate of the tool thermal deformation recognition system is about 87.2%.