Fuzzy Clustering and Visualization Analysis of Tool Wear Status Recognition

One of the biggest problems in manufacturing is the failure of machine tools which due to loss of surface material in cutting operations. Therefore, an effective diagnosis mechanism is necessary for the tool condition monitoring so that production loss and downtime can be avoided. For this, signals acquired from vibration and force sensors were processed to monitor the status of the tool wear. This paper explores the use of Frequency Band Energy (FBE) analysis and Fuzzy Clustering (FC) techniques for tool wear status recognition in metal cutting. In the first stage of the proposed scheme, FBE based on wavelet packets decomposition is performed on cutting vibration and force signals measured on the CNC machine tools. The different stages of tool wear can enhance or inhibit the effect of different frequency components. It made the extracted features sensitive to tool wear. The recognition method for tool wear status was studied through Fuzzy C-means clustering system. In order to examine the performance of clustering results, Visualization of clustering is mapped by principal component analysis (PCA). Experimental results have shown that this approach is a superior and effective method for tool wear status recognition.