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

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.

[1]  Mehmet A. Orgun,et al.  A novel fused convolutional neural network for biomedical image classification , 2018, Medical & Biological Engineering & Computing.

[2]  Huizhong Yang,et al.  Robust point‐to‐point iterative learning control with trial‐varying initial conditions , 2020, IET Control Theory & Applications.

[3]  Kaibo Shi,et al.  Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems , 2022, Appl. Math. Comput..

[4]  R. Asahi,et al.  Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics , 2017 .

[5]  Fei Liu,et al.  Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems , 2021, Sci. China Inf. Sci..

[6]  Y. Moon,et al.  Application of the Deep Convolutional Neural Network to the Forecast of Solar Flare Occurrence Using Full-disk Solar Magnetograms , 2018, The Astrophysical Journal.

[7]  E. Krimsky,et al.  Quantification of damage and its effects on the compressive strength of an advanced ceramic , 2019, Engineering Fracture Mechanics.

[8]  Cao Vu Dung,et al.  Autonomous concrete crack detection using deep fully convolutional neural network , 2019, Automation in Construction.

[9]  Umberto Michelucci Cost Functions and Style Transfer , 2019 .

[10]  Romesh Nagarajah,et al.  Particle Swarm Optimization approach to defect detection in armour ceramics , 2017, Ultrasonics.

[11]  Tahir Cetin Akinci,et al.  Application of Artificial Neural Networks for Defect Detection in Ceramic Materials , 2012 .

[12]  Nicola Paone,et al.  Nondestructive techniques for detection of delamination in ceramic tile: a laboratory comparison between IR thermal cameras and laser Doppler vibrometers , 1999, Smart Structures.

[13]  C. Przybyla,et al.  Failure prediction in ceramic composites using acoustic emission and digital image correlation , 2016 .

[14]  Wojciech Paszke,et al.  Robust PD-type iterative learning control for discrete systems with multiple time-delays subjected to polytopic uncertainty and restricted frequency-domain , 2021, Multidimens. Syst. Signal Process..

[15]  Samabia Tehsin,et al.  Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks , 2018, Technology in cancer research & treatment.

[16]  Jianfeng Zhao,et al.  Speech emotion recognition using deep 1D & 2D CNN LSTM networks , 2019, Biomed. Signal Process. Control..

[17]  Tao Ding,et al.  Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.

[18]  S. Rocchi,et al.  Defect detection in ceramic materials by quantitative infrared thermography , 2006 .

[19]  Tahir Cetin Akinci,et al.  The Defect Detection in Ceramic Materials Based on Time-Frequency Analysis by Using the Method of Impulse Noise , 2011 .

[20]  Tahir Cetin Akinci,et al.  Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks , 2020, Neural Computing and Applications.

[21]  吳在熙 CERAMICS , 1986, Arkansas Made, Volume 1.