Identification Of Flaws Research Articles

Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method

It's well-known that industrial safety is now a top concern. Nowadays, accidents caused by flammable gases occur frequently in our everyday lives. Gas cylinders, which are used for household purposes, wide range of businesses, and vehicles are often reported to be on the verge of exploding. Explosions have left a large number of individuals seriously wounded or could also be lethal in certain cases. This project's goal is to use a HOG features for SVM classifier which is used to identify pipeline gas leaks and keep tabs on them. In addition, the system utilises an image processing technique to identify pipeline fractures. Early detection and identification of pipeline flaws is a predominant aspect of this study. According to the suggested design, the robot capture the image down the pipe, looking for any signs of gas leakage by the Eddy Current method. This type of recognition has proved superior to other traditional methods. The methods with efficiency parameters and the results were compared and are tabulated in the results section. In the future, the data in the course of detection could be sent through GSM to a mobile application.

Data-driven insight into the puzzle-based cybersecurity training

Puzzle-based training is a common type of hands-on activity accompanying formal and informal cybersecurity education, much like programming or other IT skills. However, there is a lack of tools to help the educators with the post-training data analysis.Through a visualization design study, we designed the Training Analysis Tool that supports learning analysis of a single hands-on session. It allows an in-depth trainee comparison and enables the identification of flaws in puzzle assignments. We also performed a qualitative evaluation with cybersecurity experts and students. The participants apprised the positive influence of the tool on their workflows. Our insights and recommendations could aid the design of future tools supporting educators, even beyond cyber security.

Finite cell method for detection of flaws in plate structures using dynamic responses

Finite cell method (FCM) is an efficient numerical method for analysis of continuum structures with geometrical complexity, which uses the concept of virtual domain method. The use of this concept makes the mesh generation easier such that simple uniform meshes can be employed, and hence mesh refinement is not necessary for the problems like damage detection. Nevertheless, the FCM has not yet been used for damage detection of structures. In this paper, the FCM is proposed for structural damage identification. The identification of flaws based on dynamic response of the structure is investigated with the FCM and particle swarm optimization (PSO) algorithm. The FCM is utilized to model the flawed structures, while the PSO —as a well-known non-gradient-based optimization method— is employed for finding the flaw location. For the damage detection problem, the PSO explores the search space in order to detect the geometrical properties of flaws as well as the number of flaws by minimizing an error function in terms of sensor measurements. Four numerical examples including benchmark test cases are provided to demonstrate the capability of the FCM and the PSO algorithm in damage detection of plate structures.

APPLICATION OF INCLINED LONGITUDINAL ULTRASONIC WAVES TO IMPROVE THE EFFICIENCY OF RAIL FLAW DETECTION

The article is devoted to the question of rail ultrasonic testing confidence. At present there is a range of flaw orientation angles in within which the testing is absent and, therefore such flaws can be lost. To except this drawback the current sound testing scheme in flaw detector railcars is to be supplemented with inclined probes exciting longitudinal wave. Experimental checks using echomethod showed the good results on an October railway section with flaw models and on a loaded Zabaycalskaya railway section. The next step was idea of mirror-shadow method using as well as echomethod by turning of probes towards each other. Envelope analysis of bottom signals allows receiving additional information about flaws: location depth of its center and form. But using of pair of probes was not enough for unambiguous definition of other geometric flaws characteristics. That is why the bottom signal envelope of straight probe was to be analyzed additionally. A mobile (as well removable) searching system of rail checking means has such probe as a rule. In the process of writing the article previously obtained formulas to calculate depth, inclination angle and plane size of revealed flaw were modified. Values changing during testing process are absent now. Algorithm of flaws identification on the basis of bottom signals envelope is given. The approach is confirmed by mathematical modeling in CIVA-UT.

On the improvement of methodology of tax planning on the regional level

The subject of this research is the methodology of planning tax revenues at the meso-level. Tax planning at the meso-level is a complex process, since each region of the Russian Federation has its individual set of factors that influence the actual amount of tax revenues. The problem of regional differentiation by the level of socioeconomic development leads to inaccurate planning due to the fact that the current methodology takes into account a limited number of contributing factors. Therefore, the existing methodology of planning tax revenues is viewed in through the prism of multifactorial nature of regional economy. The article employs the mathematical and empirical methods for identification of flaws in the existing methodology of planning tax revenues coupled with the problem of regional differentiation of by the level of socioeconomic development. As a result, the author presents a unified approach that allows improving existing methodology of planning tax revenues and balancing the effect upon the outcome of such planning that occurs due to regional differentiation by the level of socioeconomic development. The essence of this approach lies in creation of the multivariate model of planning tax revenues that takes into account the unlimited factors affecting the amount of tax revenues from the region. The results of the multivariate model indicate the low level of error in comparison to the calculated data with the actual data. Thus, the error for income tax totaled to 1.1%, for personal income tax to 1.2%, for excise taxes to 3.4%, and for MET to 1.4%. The acquired results indicate an improvement in the quality and efficiency of tax planning on the regional level. The presented model also illustrates the factors that affect the actual amount of tax revenues, which in turn, accurately points at the factors o0f errors in the plan values. This model may considerably combine the principles of multifactorial nature, integrity, and transparency of tax planning.

Auxiliary Particle Filter–Based Multisensor Data Fusion for Quantification of Corrosion in Aerostructures

Abstract The identification of structural flaws and subsequent management is significant for health monitoring of aviation structures. Wall thickness loss can occur because of corrosion in exposed aerostructures, thereby reducing the strength of the structure. This may impose prolonged downtimes for repair and replacement activities if not detected at early stages. Eddy current testing (ECT) is a viable technique to detect such anomalies. Fusing multifrequency data produces better results because of complementary features of each contributing mode. Particle filter–based methods have already been proposed to detect and quantify flaw profiles of corrosion damage to an aircraft structure using a multifrequency ECT data fusion technique. However, to address the associated drawbacks, an auxiliary particle filter (APF) method is proposed here. In APF, the proposed importance density is a mixture of the prior state and the latest measurements, yielding focused estimation into a high-likelihood region. Simulation results are presented to demonstrate effectiveness of the proposed technique to quantify damages in aerostructures.

Correction to: Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning

A correction to this paper has been published: https://doi.org/10.1007/s11665-021-05749-6

On violation of the procedure for transaction of punishments in the sanctions of norms of the Special Part of the Criminal Code of the Russian Federation

This article provides the results of research of the system of sanctions of the Special Part of the effective Criminal Code of the Russian Federation. The object of this study is the social relations arising in temrs of infliction of criminal punishment. The subject of is sanctions of the norms of the Special Part of the effective Criminal Law of the Russian Federation. The goal consists in identification of flaws in establishment of such sanctions, substantiation of the negative impact of these flaws, and development of recommendations for their eliminations. Particular attention is given to the rules for establishment of sanctions of the norms of the Special Part of the Criminal Code of the Russian Federation, namely the procedure for the transaction punishments in the sanctions of norms of the Special Part of the Criminal Code of the Russian Federation and consequences of violating this order. The scientific novelty of this research consists in identification of violations in the sanctions of norms that have recently come into legal force. The author also determines the violations that create internal contradictions within the effective criminal law. The three groups of violations of the procedure for transaction of punishments in the sanctions of norms of the Special Part of the Criminal Code of the Russian Federation on various grounds have been distinguished. In the course of this study, the author detected fifteen violations of the rules for establishment of the sanctions of norms of the Special Part of the Criminal Code of the Russian Federation, as well as offered solutions for their eliminations. The acquired results are of practical significance and can contribute to the improvement of the Russian criminal law.

Application of a Particle Filter to Flaw Identification for Ultrasonic Nondestructive Evaluation: Assimilation of Simulated and Measured Data

The assimilation of simulated and measured data is essential for advancing technology in NDE 4.0. In this study, a particle filter (PF) was applied to identify the geometry of flaws for ultrasonic nondestructive testing. A PF based on a probabilistic approach that allows errors in measurement and simulation models may be of great assistance for the data assimilation. In the PF, state variables are expressed by random data samples called particles, together with their associated weights. The PF estimates the probabilistic density function of the state variables by merging simulation data with measured data. Data types must be physically identical in the simulation and measurement to enhance the accuracy and to accelerate the convergence speed in the PF. Here, the scattering component, which is specific information related to the flaw geometry, was used for the likelihood evaluation in the PF. The simulation, which needed many particles, was conducted using the elastodynamic finite integration technique accelerated by parallel computing with graphics processing units. The proposed PF approach was demonstrated in ultrasonic measurement, and the geometries of artificial flaws in aluminum specimens were identified using only one pulse-echo signal at a single transducer.

Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning

In additive manufacturing (AM), internal flaws that form during processing can have a detrimental impact on the resulting fatigue behavior of the component. Nondestructive x-ray computed tomography (XCT) has been routinely used to inspect AM components. This technique, however, is limited by what is resolvable as well as the automated procedures available to analyze the data. In this study, we compared XCT scans and automated flaw recognition analysis of the corresponding data to results obtained from an automated mechanical polishing-based serial sectioning system. Although internal porosity and surface roughness were easily observed by serial sectioning with bright-field optical microscopy, the same level of information could not be obtained from the XCT data. For the acquisition parameters used, XCT had only a 15.7% detection rate compared to that of serial sectioning. The results point to the need to recognize the limitations of XCT and for supplementary XCT scan quality metrics in addition to the voxel size.

On-line identification of incipient flaws in underground cables using single-end sheath currents

On-line identification of incipient flaws in underground cables using single-end sheath currents

Identifying bonding interface flaws in CRTS III type ballastless track structure using the impact-echo method

The bonding interface is a part of China Rail Track System III (CRTS III) type ballastless track structure that is prone to concealed flaws. The bonding interface is between the self-compacting concrete (SCC) filling layer and the steam-cured concrete precast plate. It is very important to detect the quality of the bonding interface. This paper investigates the feasibility of using the impact-echo method to detect the bonding interface flaws. The testing identification results of the bonding interface via the impact-echo method is compared with the actual quality state obtained by on-site uncovering plate. The main factors affecting the corresponding results are analyzed by field experiments. And the spectra of the impact-echo method under different flaw conditions are discussed. The relationship between the spectra and the actual flaws is analyzed in detail. A flaw identification function based on spectra is established to effectively identify bonding interface flaw types. This work will provide important technical supports for developing a reliable non-destructive testing method to inspect the bonding interface quality state.

Topology optimization course for mechanical engineering students

Topology optimization is becoming increasingly important in modern design of load-carrying parts. Formulating and solving topology problems correctly and efficiently is becoming one of the most important skills for students planning to become design engineers. This paper focuses on the content and implementation layout of a course intended for future design engineers. The course is founded on the so-called three-step topology optimization procedure consisting of model preparation, optimization process, and model finalization. By utilizing discussion and simple calculations, the students are first made comfortable with the most fundamental notions from the field of structural optimization. Afterwards, the most modern and advanced software solutions are engaged step by step on a suitable model example to illustrate how the optimization-and-model-correction design loop works. In this process the most critical issues, such as for example, identification of model flaws and how these are reflected on the partially optimized design, are discussed one by one. At the end of the course, the student should be equipped with skills and competences needed to engage efficiently modern topology optimization to design lightweight and durable load-carrying structural parts.

Precision manufacturing technology and measurements in steel rolling industry using multi threshold fast fourier transform and back propagation algorithm for analysis of X-ray diffractometry

X-ray diffractometry is a unique technique and that the X-ray diffraction patterns which depict the structure of the steel sheets during processing with the features extracted, that they serve directly as a signature which is very complicated. X-ray diffraction (XRD) techniques are a type of non-destructive method of investigation to identify the flaws during the fabrication of steel sheets. X-ray diffraction is comparatively simple and can be effectively used for the examination and identification of flaws during the rolling process of steel sheets. XRD technique finds application in various fields like textile industry, forensic, qualitative and quantitative phase analysis of poly crystalline material, to infer overall properties of the fiber and measure the degree of crystalline nature. It is extensively used to explore areas like material science, chemistry and in industry for research and quality control. This effective method gains novelty by combining the signal processing algorithms like multiple threshold based Fast Fourier Transform (FFT) and Artificial neural network (ANN) trained with Back Propagation Algorithm (BPA) thereby offering an automated system for online monitoring during fabrication of flawless metal sheets. The hot rolled steel sheets for three categories namely, flawless, moderate flaw and extreme flaw conditions are obtained from the XRD pattern. Then multiple thresholds are incorporated to identify the peak position, peak width and peak intensity. The FFT algorithm computes the power spectrum which is used as features to identify the flaws in the steel sheets during cold rolling process. The extracted features are used as inputs to train the ANN with BPA whose performance is evaluated to be 90% efficient.

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Abstract. Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particularly as a failure within this field can result in damages and fatalities. During assembling and soldering of printed circuit boards (PCBs) the circuit carriers can be subject to errors. Hence, automatic optical inspection (AOI) systems are used for real-time detection of visible flaws and defects in production. This article introduces an application strategy for combining a deep learning concept with an optical inspection system based on image processing. Above all, the target is to reduce the risk of error slip through a second inspection. The concept is to have the inspection results additionally evaluated by a convolutional neural network. For this purpose, different training datasets for the deep learning procedures are examined and their effects on the classification accuracy for defect identification are assessed. Furthermore, a suitable compilation of image datasets is elaborated, which ensures the best possible error identification on solder joints of electrical assemblies. With the help of the results, convolutional neural networks can achieve a good recognition performance, so that these can support the automatic optical inspection in a profitable manner. Further research aims at integrating the concept in a fully automated way into the production process in order to decide on the product quality autonomously without human interference.

Ultrasonic Flaw Recognition by Multi-Angle Phased Array Data Integration

Abstract This study presents a method of ultrasonic flaw identification using phased array ultrasonic inspection data. Raw data from each individual channel of the phased array ultrasonic inspection are obtained. The data trimming and de-noising are employed to retain the data within the boundary of the inspected object and remove the speckle noise components from the raw data, respectively. The resulting data are passed into a sequence of signal processing operations to identify embedded flaws. A shape-based filtering method is proposed to reduce the intensity of geometric noise components due to the non-uniform microstructures introduced in the manufacturing process. The resulting data matrices are integrated to obtain the intensity matrix of the possible flaw regions. Thresholding is applied to the intensity matrix to obtain the potential flaw regions, followed by a connected component analysis to identify the flaws. The overall method is demonstrated and validated using realistic phased array experimental data.

Experimental practices required to isolate thermal effects in plasmonic photo-catalysis: lessons from recent experiments

Metal nanoparticles have recently been shown experimentally to speed up chemical reactions when subject to illumination. The mechanisms of this phenomenon have been under debate. A dominant role for high energy non-thermal (typically but imprecisely referred to as “hot”) electrons was proposed in a study by the Halas group [Science 362, 69 (2018)10.1126/science.aat6967]. However, evidence that the faster chemistry has a purely thermal origin has been accumulating, alongside the identification of methodological and technical flaws in the theory and experiments claiming the dominance of “hot” electrons [Science 364, eaaw9367 (2019)10.1126/science.aaw9367]. Here, we advance this discussion towards the possibility of isolating thermal from non-thermal effects. We detail a series of experimental aspects that must be accounted for before effects of “hot” electrons can be distinguished from thermal contributions in plasmonic photocatalysis.

Fractography of silicon nitride based ceramics to guide process improvements

Fractography is an important tool to understand and identify the cause of the failure in materials. This understanding can be used to make changes in raw materials selection and processing to increase the strength of brittle materials. This study reports the fracture behavior of hot-pressed silicon nitride based ceramics, with focus on dominant flaw identification with respect to material and process parameters. Silicon nitride is an important material for structural applications which require high strength and wear resistance, such as bearings, nozzles, and cutting tools. Silicon nitride with a target base composition of Si6-zAlzOzN8-z (z = 0.5), along with varying boron dopant levels, was explored in this work. Detailed fractographic analyses revealed that the majority of fracture origins were internal flaws due to the foreign impurities introduced at various stages of processing. All materials were found to have reasonably high strength (800−1100 MPa). Strength was inversely proportional to the square root of the flaw size, however no correlation was found between measured flexural strength and fracture origin types. Mirror constants calculated from fracture mirror measurements ranged between 5.8 and 9.8 MPa.m1/2.

Creation of comfortable living environment for senior people in the context of strategic documents of the Russian Federation

The object of this research is the comfort of living environment for people of senior age. The subject of this research is the strategic, normative, and methodological documents of the Russian Federation concerning senior people. In the context of strategic documents of the Russian Federation, special attention is given to the problem of creating a comfortable living environment for senior people as a growing new group of consumers. The relevance of this research is substantiated by the worldwide tendency of population ageing alongside the growing importance of personal comfort within the system of worldview upon the formation of urban and residential environments. The scientific hypothesis is advanced that the increased comfort of the living environment for senior people promotes active longevity and improvement of the quality of life, which in turn, is the key goal of the Russian strategic government programs. The article analyzes the strategic, normative, and methodological documents of the Russian Federation on the improvement of comfort of living environment for people of senior age. The author's contribution to this research consists in identification of flaws in the mechanisms of formation of comfortable living environment for senior people in the Russian Federation, and offers solutions to this issue. Senior citizens should be recognized as a special and expanding group of consumers that opens a new niche in the market of goods and housing services in form of personal specialized housing integrated into the urban development. The author also believes that it is necessary to integrate new urban planning and organizational-administrative decisions on the basis of methodological construction documents, as well as documents on the development residential construction in the Russian Federation, into the strategic tasks on supporting senior population. In order to develop the reform mechanism for the creation of comfortable living environment, it is necessary to consolidate certain actions on the federal level.

Elastic least-squares reverse-time migration with density variation for flaw imaging in heterogeneous structures

Ultrasonic techniques are able to accurately detect and characterize flaws in homogeneous structures. Elastic reverse time migration (ERTM) is a powerful tool to reconstruct high-resolution images of flaws. To achieve images with better quality, the solution can be obtained by iteratively finding an image generating the modeled data which can best match the measured data in a least-squares sense, i.e. least-squares migration (LSM). Combing ERTM and LSM, conventional elastic least-squares reverse time migration (ELSRTM) methods are based on the assumption of a constant density, which can lead to inaccurate amplitudes and parameter crosstalk artifacts in the reconstructed images. In this paper, an ultrasonic imaging technique based on the ELSRTM which considers density as well as longitudinal-(L-) and shear-wave (S-wave) velocity variations is explored for imaging flaws in heterogeneous structures. The ELSRTM with density variations can simultaneously reconstruct density and L- and S-wave velocity images, which can provide amplitude-preserving images and mitigate crosstalk artifacts. This method is applied to numerical as well as physical laboratory experiments and the results appear promising for flaw identification in heterogeneous structures.