This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

本文介绍了一种新开发的用于铣削颤振检测的多传感器数据融合，与传统的颤振检测方案相比，该方案具有廉价且易于实现的特点。所提出的多传感器数据融合利用麦克风和加速度计传感器来测量铣削过程中颤振的发生。与测功机相比，具有安装方便、成本低的优势。在本文中，采用小波包分解来分析测量的声音和振动信号。然而，小波包分解的参数需要微调以提供良好的性能。因此，通过优化小波包分解参数的选择，包括母小波和基于峰度和波峰因子的分解级别，改进了所开发方案的结果。此外，使用递归特征消除方法选择重要的颤振特征，并将其性能与元启发式算法进行比较。最后，采用了几种机器学习技术来根据所选特征对切削稳定性进行分类。结果证实，所提出的多传感器数据融合方案可以在工业条件下提供有效的颤振检测，并且比传统方案具有更高的精度。并将其性能与元启发式算法进行比较。最后，采用了几种机器学习技术来根据所选特征对切削稳定性进行分类。结果证实，所提出的多传感器数据融合方案可以在工业条件下提供有效的颤振检测，并且比传统方案具有更高的精度。并将其性能与元启发式算法进行比较。最后，采用了几种机器学习技术来根据所选特征对切削稳定性进行分类。结果证实，所提出的多传感器数据融合方案可以在工业条件下提供有效的颤振检测，并且比传统方案具有更高的精度。