Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: A PCA-based approach

During the last years, notable efforts have been made to develop reliable and industrially applicable machining monitoring systems based on different types of sensors, especially indirect methods that do not require the interruption of the machining process. As the main objective in machining processes is to produce a high-quality surface finish which, however, can be measured only at the end of the machining cycle, a more preferable method would be to monitor the quality during the cycle. Motivated by that premise,do not interrupt the machining process, results of investigation on the relationship between audible sound emitted during process and the resulted surface finish are reported in this paper. Through experiments with AISI 52100 hardened steel, this paper shows that such a correlation does exist between the surface roughness and the sound energy, and based on that correlation, a new quality monitoring method is proposed using Support Vector Machines (SVM) approached by the Principal Component Analysis (PCA). Obtained results shown that this method can identify three different levels of surface roughness achieving an average accuracy of 100.00%. (C) 2019 Elsevier Ltd. All rights reserved.