在铣削加工工程中,产生颤振,严重影响产品的加工精度和表面质量。 为了有效避免铣削过程中发生颤振,提出了基于 自适应调频模态追踪(adaptive chirp mode pursuit,ACMP)的铣削颤振监测和识别方法。 该方法综合考虑了振动信号的带宽和 微弱特性,ACMP 在递归框架中逐个捕获信号模式,在该算法中,不需要输入信号模式的个数,而是可以通过评估残差信号的能 量来学习,这样就可以避免由于分解层数不确定带来的模态混叠或者过度分解的问题。 首先使用仿真信号验证了该算法对颤 振信号具有很高的识别精度;然后基于现场的铣削实验数据证明该方法及时有效地对颤振进行识别;最后从 ACMP 处理后的 信号中提取功率谱熵值作为颤振识别特征。 该方法解决了经验模态分解(empirical mode decomposition,EMD)算法的模态混合 和伪分量问题,又降低了变分模态分解( variational mode decomposition,VMD)的精度不稳定的影响,可以准确快速地识别到颤 振,对提高加工质量具有重要意义。
In milling machining engineering, chattering is generated, which seriously affects the machining accuracy and surface quality of products. In order to effectively avoid chattering during milling, a milling chattering monitoring and identification method based on Adaptive chirp mode pursuit (ACMP) is proposed. The method integrates the bandwidth and faint characteristics of vibration signals, and ACMP captures the signal modes one by one in a recursive framework. In this algorithm, we do not need to input the number of signal modes, but can learn them by evaluating the energy of the residual signal, so that we can avoid the problems of modal mixing or over-decomposition due to the uncertainty of the number of decomposition layers. Firstly, the algorithm is verified to have high recognition accuracy for chattering signals using simulated signals; then the method is demonstrated to be effective in recognizing chattering in time based on field milling experimental data; finally, the power spectrum entropy value is extracted from the ACMP processed signals as chattering recognition features. This method solves the modal mixing and pseudo-component problems of empirical mode decomposition (EMD) algorithm, and reduces the influence of unstable accuracy of variational mode decomposition (VMD), which can accurately and quickly identify the chattering and is of great significance to improve the machining quality.