Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data

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.