Abstract
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.
Highlights
Increasing the variety of quality products and reducing costs in the ceramic industry is economically prioritized and important
The pre-trained deep CNN (DCNN) Alexnet model was used for crack detection in ceramic plates
It is understood that the DCNN model is more cost-effective compared to infrared (IR), thermographics, X-ray methods for examining the internal structures of ceramic materials, it gives more rapid results and is more likely to be preferred in ceramic technology in the future
Summary
Increasing the variety of quality products and reducing costs in the ceramic industry is economically prioritized and important. The. Faults and cracks in ceramic materials may have various reasons. Faults and cracks in ceramic materials may have various reasons These deformations can be determined by examining the structural properties of ceramic materials. The defects in a ceramic body [4, 5] are usually caused by faulty process parameters or uncontrolled raw materials during the pressing process [6]. Such defects generally include delaminating heterogeneous materials or agglomerates which reduce structural strength. Different methods are used in the detection of cracks in ceramic, porcelain or cemented materials. Morscher and Han examined the differences in the acoustic emission waveform to predict
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