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      Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

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          Abstract

          Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.

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          An efficient k-means clustering algorithm: analysis and implementation

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                23 June 2021
                July 2021
                : 21
                : 13
                : 4288
                Affiliations
                [1 ]Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan; 4a813033@ 123456gm.student.ncut.edu.tw (U.R.); s4a913019@ 123456student.ncut.edu.tw (Y.-Z.C.)
                [2 ]Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; t108318008@ 123456ntut.org.tw
                Author notes
                [* ]Correspondence: peng@ 123456ncut.edu.tw
                Author information
                https://orcid.org/0000-0002-1603-5190
                Article
                sensors-21-04288
                10.3390/s21134288
                8296841
                34201656
                4d614015-9fb7-407f-8e2e-d5353765076f
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 10 May 2021
                : 16 June 2021
                Categories
                Article

                Biomedical engineering
                sound detection,monitoring,cnc milling machine,fast fourier transform,k-means

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