Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

数控机床中的刀具状态监视（TCM）在确保高制造质量方面起着至关重要的作用。根据从各种传感器中的一个或多个传感器获得的数据进行TCM处理，其中声学传感器具有许多实际优势。但是，声传感器数据会受到强烈的干扰，这会严重限制有关工具状况的预测准确性。本工作通过提出一种新颖的TCM方法来解决这个问题，该方法仅采用了声传感器信号的一些适当特征参数，并结合了两层角度内核极限学习机。两层网络结构用于增强对复杂非线性数据相关特征的学习，为了避免与常规内核函数中使用预设超参数相关的复杂性，采用了两个不带超参数的角度内核函数。实验证明了所提出的TCM方法相对于其他基于声音传感器数据的最新技术具有优异的TCM性能。