One of the most important parameters in machining process is tool wear. Thus, monitoring the wear of cutting tools is essential to ensure product quality, increase productivity, reduce environmental impact and avoid catastrophic damages. As wear is related to the vibrations of the process, the vibration signal is commonly used to monitor the process non-intrusively. Traditional wear monitoring techniques present a number of problems such as: the difficulty of identifying vibration features sensitive to wear evolution, the specialist requirement for supervising the model training and an endless series of tests to work with balanced data. To overcome these difficulties, this paper aims to propose a new approach in the application of unsupervised artificial intelligence technique with imbalanced data to identify the cutting tool wear condition during the turning process. The methodology will allow industrial applications since no supervision is required in the model training when machining condition is changed. From vibration signals collected during each tool pass, a self-organizing map model was used to identify the ideal moment of tool change. The classifier used was compared to benchmark supervised methods (weighted k-nearest neighbor and support vector machine). Imbalanced data sets were used to simulate the industrial reality. Tool tests were performed under different wear conditions and changing the cutting parameters. The results showed that it is possible to predict the cutting tool wear condition with a self-organizing map neural for imbalanced data, using only the vibration signal with up to 92% accuracy.

加工过程中最重要的参数之一是刀具磨损。因此，监控切削工具的磨损对于确保产品质量，提高生产率，减少环境影响并避免灾难性损害至关重要。由于磨损与过程的振动有关，因此振动信号通常用于非侵入式地监视过程。传统的磨损监测技术存在许多问题，例如：难以识别对磨损演变敏感的振动特征，监督模型训练的专业要求以及使用平衡数据进行的一系列测试。为了克服这些困难，本文旨在提出一种新的方法，在不监督数据的无监督人工智能技术中，用于识别车削过程中的刀具磨损状况。该方法将允许工业应用，因为在改变加工条件时无需在模型训练中进行监督。根据每次走刀过程中收集到的振动信号，使用自组织映射模型确定理想的换刀时刻。将使用的分类器与基准监督方法（加权k最近邻和支持向量机）进行比较。不平衡数据集用于模拟工业现实。刀具测试是在不同的磨损条件下进行的，并更改了切削参数。