DWT/ MFCC Feature Extraction for Tile Tapping Sound Classification

Tile tapping sound inspection is a process of construction quality control. Hollow sound, for instance, indicate low quality tessellation and thus voids underneath that could lead to future broken tiles. Hollow-sounding inspection was often carried out by construction specialists, whose skills and judgment may vary across individual. This paper elevates this issue and presents a Deep Learning (DL) classification method for computerized sounding tile inspection. Unlike other existing works in the area, where structural details were assessed, this study acquired tapping sound signals and analyzed them in a spectral domain by using Discrete Wavelet Transform (DWT) and Mel-frequency Cepstral Coefficients (MFCC). The dull versus hollow sounding tile were then classified based on these features by means of a Convolutional Neural Network (CNN). The experiments carried out in a laboratory tessellation indicated that the proposed method could differentiate dull from hollow-sounding tiles with very high accuracy up to 93.67%. The developed prototype can be used as guideline for devising a tiling inspection standard

A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, and Transfer Learning

ABSTRACT A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, Machine Learning Including Transfer Learning Farzad Kashefinishabouri Fiber-reinforced polymer composites have garnered interest in a range of industrial applications due to their outstanding mechanical characteristics and lightweight. Monitoring the health of polymer composite structures in real-time is one of the most challenging issues in the practical use of composites due to their susceptibility to many types of damage. Conventional non-destructive tests (NDT) methods such as X-ray tomography and ultrasonic may be used to assess composite materials but the drawback of conventional NDT techniques is that they cannot be implemented when the composite part is in use. Composite plates’ electrical characteristics can be to do real-time health monitoring. Carbon nanotubes (CNTs) because of their excellent conductivity characteristics, are used in composites to enhance the conductivity of resins within composites. It is shown that by monitoring the electrical behavior of composites with CNT embedded resins, defects can be detected. The issue with this approach is that numerous wires and connections are required. The weight of composite structures is greatly influenced by the number of wires and connections, which also makes the system more prone to errors because numerous connections must function well for the system to respond as intended. To tackle this problem, in this study, the goal is to reduce the number of required probes and connections by limiting the probes to the edges of composite plates rather than throughout the plate. By having the probes on the edges of the plate, there may not be a direct correlation between defects at different locations within the plates to the measurements as it was in the previous cases. There are two approaches to tackle this problem:1. To develop a physics-based model that can precisely model the electrical behavior of composite with CNTs embedded in the resin. 2. To develop a data-driven model that can relate the measurements to the location of the defect. As the first approach is expensive and time-consuming the second approach is picked in this study. Neural network (NN) is used to find the pattern between measurements on the edges and the location of the defect. The problem with using neural network (NN) models is that they require numerous numbers of labeled examples. To tackle this problem Transfer Learning (TL) and data augmentation is used. TL is used to reduce the number of labeled data points required for the training process as in composites it is too expensive and time-consuming to generate a huge number of data points. In the TL method that is used, the training is done in two stages, first stage the training is done based on the data generated from a similar problem that the data can be abundant, and the second stage of training is done using experimental data of the exact problem. Data augmentation is used on experimental data to increase the data points for training NN. The performance of the trained neural network in locating defects by having the probes only on the edges of the samples is promising (accuracy of 78.57% on the test set). Also, the performance of the neural network models for different precisions and sample sizes were studied. The precision is defined as the area in which the defects can be located within. KEYWORDS Real-time defect detection, Neural Network, Transfer learning, Data augmentatio

Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review

Composite materials/structures are advancing in product efficiency, cost-effectiveness and the development of superior specific properties. There are increasing demands in their applications to load-carrying structures in aerospace, wind turbines, transportation, and medical equipment, etc. Thus robust and reliable non-destructive testing (NDT) of composites is essential to reduce safety concerns and maintenance costs. There have been various NDT methods built upon different principles for quality assurance during the whole lifecycle of a composite product. This paper reviews the most established NDT techniques for detection and evaluation of defects/damage evolution in composites. These include acoustic emission, ultrasonic testing, infrared thermography, terahertz testing, shearography, digital image correlation, as well as X-ray and neutron imaging. For each NDT technique, we cover a brief historical background, principles, standard practices, equipment and facilities used for composite research. We also compare and discuss their benefits and limitations, and further summarise their capabilities and applications to composite structures. Each NDT technique has its own potential and rarely achieves a full-scale diagnosis of structural integrity. Future development of NDT techniques for composites will be directed towards intelligent and automated inspection systems with high accuracy and efficient data processing capabilities

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented

Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.This research has been funded by Ministry of Science and Innovation (Government of Spain) through the research project titled Fusion of nondestructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00

Дослідження зображень інфрачервоного неруйнівного контролю полімерних композитів, армованих скловолокном

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник роботи: професор, д.т.н. Карускевич Михайло ВіталійовичWith the development of society, science, and technology, various industries have higher and higher requirements for the resolution of images. If the resolution is not high, it may bring negative effects and difficulties to some jobs. Super-resolution reconstruction technology is a method that does not need to upgrade the hardware level to improve image resolution. Before the advent of deep learning methods, people mostly used traditional techniques such as interpolation to super-resolution images, but this method is very limited. The generated images are also unsatisfactory, especially in the face of a large number of pictures, this algorithm cannot be upgraded and optimized in a targeted manner, and super-resolution based on deep learning can solve this problem well, but not all deep learning super-resolution methods have excellent results. In order to solve this problem, this paper proposes a super-resolution algorithm based on generative adversarial networks.З розвитком суспільства, науки та технологій у різних галузях промисловості висуваються все вищі вимоги до роздільної здатності зображень. Якщо роздільна здатність невисока, це може призвести до негативних наслідків і ускладнень для деяких робіт. Технологія реконструкції з надвисокою роздільною здатністю – це метод, який не потребує оновлення апаратного рівня для покращення роздільної здатності зображення. До появи методів глибокого навчання люди здебільшого використовували традиційні методи, такі як інтерполяція до зображень із надвисокою роздільною здатністю, але цей метод дуже обмежений. Згенеровані зображення також незадовільні, особливо в умовах великої кількості зображень, цей алгоритм не можна цілеспрямовано оновити й оптимізувати, а супер-роздільна здатність на основі глибокого навчання може добре вирішити цю проблему, але не всі методи супер-глибокого навчання мають чудові результати. Щоб вирішити цю проблему, у цій роботі пропонується алгоритм супер-роздільності, заснований на генеративних змагальних мережах

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation