Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastian, Spain.
Faculty of Engineering, Mondragon University, 20500 Mondragon, Spain.
Sensors (Basel). 2021 Sep 6;21(17):5984. doi: 10.3390/s21175984.
There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.
业界越来越倾向于实时了解其资产状况。特别是，刀具磨损是一个关键方面，需要实时监测以降低加工过程中的成本和废品率。传统上，为了预测加工中刀具磨损状况，已经开发了数学模型来从机器上附加的传感器信号中提取信息。为了降低开发物理模型的复杂性，这需要对所建模系统有深入的了解，当前的趋势是使用基于来自刀具磨损的数据的机器学习 (ML) 模型。声发射 (AE) 技术已广泛用于捕获数据并了解切削刀具等工业资产的实时状况。然而，AE 信号的解释和处理相当复杂。从 AE 信号中提取的最常见的特征之一是计数参数，定义为信号幅度超过预设阈值的次数。此特征的一个常见问题是定义适当的阈值以获得一致的磨损预测。此外，AE 信号带宽相当宽，许多作者指出，选择最佳频率带宽进行特征提取是关键且复杂的。为了克服这些问题，本文提出了一种使用小波包变换在多分辨率水平上应用多阈值计数特征提取的方法，该方法从 AE 信号中提取冗余且非最优的特征图。接下来，执行递归特征消除以减少和优化在前一步中生成的大量预测特征，并使用随机森林回归提供估计的刀具磨损。提出的方法使用在既定切削条件下车削 19NiMoCr6 钢时捕获的数据进行了测试。将获得的结果与几种 ML 算法（如 k-最近邻、支持向量机、人工神经网络和决策树）进行了比较。实验结果表明，所提出的方法可以将预测均方根误差降低 36.53%。
