Tool Condition Monitoring based on sound and vibration analysis and wavelet packet decomposition

Rafezi, Hamed; Âkbari, Jafar; Behzad, Mahdi

doi:10.1109/isma.2012.6215170

Cited by 16 publications

(7 citation statements)

References 3 publications

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“…An experienced machine operator is able to determine when optimum conditions cease to exist in the cut by simply listening [63] For example, it is not the noise level of the sound samples that is evaluated but their frequency characteristic, which is obtained using the Fourier transform. However, it is necessary first to consider which outputs are required and choose the length of the intervals to be examined accordingly [64]. However, investigating the cutting sound is one thing; the requirement to control the cutting process is another.…”

Section: Advanced Adaptive Control Methodsmentioning

confidence: 99%

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

et al. 2022

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The article aims to use the generated sound as operational information needed for adaptive control of the metalworking process and early monitoring and diagnosis of the condition of the machined materials using a newly introduced surface roughness quality index due to the sound-controlled machining process. The object of the measurement was correlation between the sound intensity generated during cutting and the material parameters of the machined surface, i.e., the roughness of the machined surface and the degree of wear of the cutting tool. The roughness was measured during longitudinal turning of a steel billet with a P25 insert made of 12X18H10T steel and a T15K6 cutting insert made of a titanium, cobalt, and tungsten group alloy. The correlation between the sound and roughness of the machined surface was 0.93, whereas between the sound and wear of the cutting tool was 0.93. The correlation between sound and tool wear in the experiment with P25 and T15K6 cutting inserts and the correlation between sound and roughness is positive.

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Section: Advanced Adaptive Control Methodsmentioning

confidence: 99%

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

et al. 2022

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“…Fast Fourier transform [ 23 ] is an algorithm used to represent internal signals discrete-time domain and frequency domain. Discussing FFT-IFFT indeed cannot be separated from discrete Fourier transform (DFT) [ 24 ]. DFT is a mathematical transformation method for discrete time signals into the domain frequency.…”

Section: Proposed System and Methodology For Sound Detection Monitoring Tool In Cnc Milling Soundsmentioning

confidence: 99%

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

Peng

Raihany

Kuo

et al. 2021

Sensors

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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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“…This establishes a robust correlation with tool wear [19]. Rafezi et al [20] monitored tool wear utilizing both acoustic and vibration signals in tandem with Wavelet Packet Decomposition (WPD). Similarly, Gomes et al [21] employed Support Vector Machines (SVM) to analyze vibration and sound signals, subsequently leveraging Recursive Feature Elimination (RFE) to discern the most pertinent features for tool wear during micro milling.…”

Section: Introductionmentioning

confidence: 99%

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Ni,

Liu,

Meng

et al. 2023

Machines

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Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%.

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Tool Condition Monitoring based on sound and vibration analysis and wavelet packet decomposition

Cited by 16 publications

References 3 publications

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

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

Identification of Tool Wear Based on Infographics and a Double-Attention Network

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