The Defect Detection in Ceramic Materials Based on Time-Frequency Analysis by Using the Method of Impulse Noise

Akıncı, Tahir Çetin

doi:10.2478/v10168-011-0007-y

Cited by 11 publications

(30 citation statements)

References 8 publications

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Section: Experimental Measurement and Data Collection Systemmentioning

confidence: 99%

“…Testing set, developed by Akinci [12], was used in this study and the data were obtained from this testing set. Pendulum was used in order to produce impact in experimental measurement and data collection systems [12,36]. An impact of determined size was inflicted to the ceramic plate through a plastic hammer connected to the end of impact pendulum and sound, emerged from the plate as a result of the impact, was recorded by the microphone and data collection system [12,37,38].…”

Section: Experimental Measurement and Data Collection Systemmentioning

confidence: 99%

“…Impacts in the same severity were applied to the ceramic plates of same type and model through pendulum and the sound data, which emerged as a result of impact, had been recorded. The output audio signal of the amplifier is transmitted to the computer at a sampling rate of 0.00001 seconds via Advantech 1716 L Multifunction PCI card and data processing is performed by using Matlab © (Figure 1) [12,38].…”

Section: Figure 1 Data Acquisition and Measurement Systems [12]mentioning

confidence: 99%

“…The sound emitted from the material by the impact applied to the ceramic material is quaint with the microphone. It is aimed to analyze sounds by recording sounds applied to ceramic materials with strong and different cracks in a similar way [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Spectral and Statistical Analysis for Damage Detection in Ceramic Materials

Akgun¹

2020

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In this study, the internal or superficial cracks that may occur during the production of ceramic plates were determined using the impact noise method. In the industry, ceramic materials are frequently used in areas such as kitchenware and construction. Many different methods are used in the quality control processes of ceramic materials. In this study, the sound produced by the impact applied to the ceramic material was analyzed. As a result of the analysis, the material was found to be undamaged or damaged. This method is called the Impulse Noise method. In this study, damaged and undamaged ceramic plates with different cracks were selected and impact plates were applied to the plates by impact pendulum. The ceramic plates used in the application have the same characteristics and dimensions produced by the same company of the same type. The noise generated as a result of the impact applied to a determined point on the tested materials was examined by Wigner-Ville method which is one of the time-frequency analysis methods. In addition, bispectrum and trispectrum analyzes, which are high-order spectral analysis (HOSA) methods, were used. Statistically, signal analysis with running minimum and maximum, mean value and peak to RMS examinations were also added. The applied methods give good results in differentiating between undamaged and damaged ceramic plates.

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Section: Experimental Measurement and Data Collection Systemmentioning

confidence: 99%

Section: Experimental Measurement and Data Collection Systemmentioning

confidence: 99%

Section: Figure 1 Data Acquisition and Measurement Systems [12]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral and Statistical Analysis for Damage Detection in Ceramic Materials

Akgun¹

2020

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“…Equation (5) is a second order differential equation describing the dynamical system of interest (Bevivino, 2009;Kater, 2008;Akinci, 2011). The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Defect Detection in Ceramic Materials

Akıncı¹,

Noğay²,

Yılmaz³

2012

Archives of Acoustics

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In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.

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Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network

Noğay

Akıncı

Yılmaz

2021

Neural Comput & Applic

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Ceramic materials are an indispensable part of our lives. Today, ceramic materials are mainly used in construction and kitchenware production. The fact that some deformations cannot be seen with the naked eye in the ceramic industry leads to a loss of time in the detection of deformations in the products. Delays that may occur in the elimination of deformations and in the planning of the production process cause the products with deformation to be excessive, which adversely affects the quality. In this study, a deep learning model based on acoustic noise data and transfer learning techniques was designed to detect cracks in ceramic plates. In order to create a data set, noise curves were obtained by applying the same magnitude impact to the ceramic experiment plates by impact pendulum. For experimental application, ceramic plates with three invisible cracks and one undamaged ceramic plate were used. The deep learning model was trained and tested for crack detection in ceramic plates by the data set obtained from the noise graphs. As a result, 99.50% accuracy was achieved with the deep learning model based on acoustic noise.

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The Defect Detection in Ceramic Materials Based on Time-Frequency Analysis by Using the Method of Impulse Noise

Cited by 11 publications

References 8 publications

Spectral and Statistical Analysis for Damage Detection in Ceramic Materials

Spectral and Statistical Analysis for Damage Detection in Ceramic Materials

Application of Artificial Neural Networks for Defect Detection in Ceramic Materials

Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network

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