Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Tool condition monitoring has found its importance to meet the requirement of production quality in industries. Machined surface texture is directly affected by the extent of tool wear. Hence, by analyzing the machined surface images, the information about the cutting tool condition can be obtained. This paper presents a novel technique for tool wear classification using hidden Markov model (HMM) technique applied on the features extracted from the gray level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull and dull tool states. The proposed method is found to be cost effective and reliable for on-machine tool classification of cutting tool wear with an average of 95% accuracy.