Types Of Flaws Research Articles

A Case Study: An Android Security

Software security has made great progress; code analysis tools perform extensive checks for code defects, it is useful to have a basic understanding of the different warnings and emphasize the bug. However, this is beyond the state of the art for many types of application security flaws. Thus, such tools frequently serve for an analyst to help them zero in on security relevant portions of code so they can find flaws more efficiently, rather than a tool that simply finds flaws automatically. In cooperation with a security expert, we carried out a case study with the mobile phone platform Android, and employed the reverse engineering tool-suite Bauhaus for this security assessment. During the investigation we found some inconsistencies in the implementation of the Android security concepts. Based on the lessons learned from the case study, we propose several research topics in the area of reverse engineering that would support a security analyst during security assessments.

Interlaboratory study of flexural strength in additively manufactured alumina

AbstractWe report the results of an international interlaboratory study of the flexural strength of alumina fabricated across six laboratories using the vat photopolymerization ceramic additive manufacturing (AM) technology. The mechanical testing of all the specimens, 142 in total, was performed at the National Institute of Standards and Technology (NIST) according to the well‐established four‐point bending method standardized for traditional ceramics. Overall, the existing ASTM standard for the four‐point bend testing proved adequate for AM ceramics, with several modifications to the specimen requirements to account for the specifics of AM processes. Critical flaws that caused failure were identified in all but two cases using optical fractography augmented with the imaging of fracture surfaces in a scanning electron microscope. The flexural strength data, analyzed following the Weibull statistics, exhibited considerable variation among the specimen sets manufactured by different laboratories. This variability correlated with the presence of many distinct critical flaws. We identified seven types of flaws that accounted for the failure of 94% of specimens. The prevalent flaws depend on the printing direction relative to the specimen's geometry. We discuss the nature of these flaws and their relation to the printing and post‐processing conditions. Removal of several types of critical flaws will significantly improve mechanical properties of ceramic parts built using vat‐photopolymerization AM.

Research on heat exchanger tube sheet to shell fillet weld testing and engineering application using total focusing phased array technique

Abstract According to structure of the heat exchanger tube sheet to shell fillet weld, the total focusing phased array technique was adopted to settle down this testing difficulty. Firstly, the inspection background and detection difficulties of the fillet weld was introduced in this paper. Secondly, the sound field simulation and typical flaw response simulation was carried out using CIVA software, and the special blocks were manufactured on the base of the simulation result. Thirdly, the total focusing phased array testing study was carried out in laboratory, and the drilled holes, grooves, as well as the typical flaws of incomplete fusion, incomplete penetration and crack in blocks were successfully detected. Finally, the total focusing technique engineering application of one heat exchanger in chemical device was put into effect, the incomplete fusion and incomplete penetration flaws were successfully detected. The total focusing phased array technique was successfully applied to the heat exchanger inspection project, the research result of this paper have high practical value for ensuring the safe and reliable operation of heat exchanger, and can be popularized and applied in the engineering project.

Developing flaw sizing methodology in Total Focusing Method (TFM) by EDM calibration blocks

The Total Focusing Method (TFM) represents a significant advancement in ultrasonic inspection, delivering high-resolution imaging by leveraging phased array technology combined with sophisticated data processing algorithms. This synergy enables detailed visualization of flaws in various materials, thereby improving flaw detection and characterization. Despite TFM's capabilities, the lack of a standardized methodology for flaw sizing limits its potential for flaw evaluation. This paper seeks to establish a new paradigm in flaw sizing by introducing a custom methodology specifically designed for TFM, using electrical discharge machined (EDM) calibration blocks that reflect a range of flaw shapes. The research highlights the limitations of conventional side-drilled holes (SDH) for capturing realistic flaw nuances and emphasizes the superior ability of EDM notches to simulate the complex geometries inherent in typical flaws. By investigating the influence of different TFM modes, the study provides insight into their effectiveness in improving the accuracy of flaw characterization. Our approach addresses the challenges of existing TFM practices, with EDM notches serving as an essential tool in methodological advancement. This work contributes to the continued development of best practices in TFM application, paving the way for more accurate, reliable, and versatile nondestructive testing.

Common Flaws in Running Human Evaluation Experiments in NLP

Abstract While conducting a coordinated set of repeat runs of human evaluation experiments in NLP, we discovered flaws in every single experiment we selected for inclusion via a systematic process. In this squib, we describe the types of flaws we discovered, which include coding errors (e.g., loading the wrong system outputs to evaluate), failure to follow standard scientific practice (e.g., ad hoc exclusion of participants and responses), and mistakes in reported numerical results (e.g., reported numbers not matching experimental data). If these problems are widespread, it would have worrying implications for the rigor of NLP evaluation experiments as currently conducted. We discuss what researchers can do to reduce the occurrence of such flaws, including pre-registration, better code development practices, increased testing and piloting, and post-publication addressing of errors.

Estimating the remaining service life of in-situ grouted post-tension anchors using flaw tolerance limit plots

Brittle failure of grouted post-tensioned anchors used in trunnion girders at dams has given rise to concerns and questions surrounding their continued use and remaining service life. This paper reviews the brittle failure of five grouted post-tensioned anchor rods due to stress corrosion cracking at dams in California. To provide an early warning system to dam owners and expand on available resources for practicing engineers, this study derives plain strain fracture toughness from the failed grouted post-tension anchors using cross sectional photos. Using derived and available fracture toughness values, flaw tolerance limit plots, which can quickly illuminate the severity of a defect for various anchor diameters, were developed for semicircular and straight fronted flaw types using linear elastic fracture mechanics and stress intensity factors for both ASTM A322 and ASTM A722 materials. To help demonstrate the use of flaw tolerance limit plots and estimate the remaining service life of an in-situ anchor, an example problem was developed.

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the frame per second is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.

Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration

The quality of calibration datasets is critical for establishing well-calibrated models for reliable decision-making support. However, the analysis of the influence of calibration dataset quality and the discussion on how to use flawed and/or incomplete datasets are still far from sufficient. An evaluation framework for the impact of model calibration data on parameter identifiability, sensitivity, and uncertainty (ISU) was established. Three quantitative and normalized indicators were designed to describe the magnitude of ISU. With the case study of the upper Daqing River watershed, China and the model SWAT (Soil and Water Assessment Tool), one ideal dataset without quality flaws and 79 datasets with different types of flaws including observation error, low monitoring frequency, short data duration and low data resolution were evaluated. The result showed that 4 of 13 parameters that control canopy, groundwater and channel processes have higher ISU values, indicating the high identifiability, high sensitivity, and low uncertainty. The largest gap of parameter ISU between dataset with quality flaw and ideal dataset was 0.61 due to short data duration, while the smallest gap was −0.28 due to low monitoring data frequency. Although some defective datasets caused unacceptable calibration results and model output, some defective datasets can still be valuable for model calibration which depends on the hydrological processes of interest when applying the model. Equivalent calibration results were yielded by the datasets with similar statistical properties. When using datasets with traditional defective issues for calibration, a new step checking the consistency among decision goal, representative system process, determinative parameters and calibration datasets is suggested. Practices including process-related data selection, dataset regrouping and risk self-reporting when using low-quality datasets are encouraged to increase the reliability of model-based watershed management.

Benefits of Non-Destructive Evaluation and Probabilistic Damage Tolerance Cosimulation for Fracture Risk Reduction

In the context of the damage tolerance approach used to drive aircraft maintenance operations, it is essential to demonstrate the reliability of NDE inspections in detecting structural damage especially for additively manufactured components since this process produces components that may introduce material anomalies at any location within a component. The Probability Of Detection curve (POD) that links the probability to detect a detrimental flaw to its size is generally used as a key indicator for that purpose by giving the maximum flaw size that a NDE process can miss with a given level of probability and confidence. This information can then be used along with other inputs such as component geometries, mechanical properties, constraints, service and residual stresses and damage evolution speed to assess fracture risk and then adapt maintenance scenarios to optimize safety and component service life. NDE reliability and risk assessment are based on statistical indicators that need a large amount of data to provide reliable metrics. It is difficult to obtain such indicators using a purely empirical approach that is based on physical trials and measurements as it may involve many mock-ups and costly processes. Simulation tools can achieve this goal via their ability to include and precisely monitor many parameters as well as high computing capacities that are now available. The work presented in this paper involved cosimulations performed between the DARWIN® probabilistic damage tolerance software and the CIVA NDE simulation software. DARWIN computes fracture risk levels throughout a component, while CIVA can efficiently provide Probability Of Detection curves at different locations in a component and for various NDE methods. The presented application deals with a titanium gas turbine engine impeller disk and involves an Ultrasonic NDE inspection technique. It appears to be a very interesting approach to connect together NDE and Fracture mechanics simulations, two disciplines that generally work on their own. Indeed, DARWIN helps the user to determine detrimental flaw types, locations and sizes which are key inputs to develop an effective NDE inspection method. CIVA can provide location-specific POD curves that enable DARWIN to quantity the effect of dedicated NDE on potential risk reduction. This highlights the importance of NDE simulation for safety and helps to identify potential changes in the physical NDE process and the maintenance cycle that provide the best compromise between detection performance and cost.

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique

Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system's maintainability. It is quite essential to assess the severity of the code smells detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances the difficulties in code smell severity detection. In this study, four code smell severity datasets (Data class, God class, Feature envy, and Long method) are selected to detect code smell severity. In this work, an effort is made to address the issue of class imbalance, for which, the Synthetic Minority Oversampling Technique (SMOTE) class balancing technique is applied. Each dataset's relevant features are chosen using a feature selection technique based on principal component analysis. The severity of code smells is determined using five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, and Logistic Regression. This study obtained the 0.99 severity accuracy score with the Random forest and Decision tree approach with the Long method code smell. The model's performance is compared based on its accuracy and three other performance measurements (Precision, Recall, and F-measure) to estimate severity classification models. The impact of performance is also compared and presented with and without applying SMOTE. The results obtained in the study are promising and can be beneficial for paving the way for further studies in this area.

Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

For wafer foundries to maintain high yield and quality, errors in manufacturing wafers must be identified and corrected. To find the source of problems and increase overall yield, defect pattern recognition (DPR) is essential. Nevertheless, mixed-type DPR, which includes identifying numerous types of flaws at once, poses more difficulties because of the variety of spatial features, the unpredictability of flaw patterns, and the fluctuating amount of flaws present. So, a novel joint attention skipped graph convolutional neural network (JAS-GCNN) technique is suggested in this paper to analyse the difficulty of mixed-type DPR. The JAS- GCNN effectively captures locally as well as globally interconnections among faults on a wafer by fusing the strength of GCNN along with focused processes. The JAS-GCNN facilitates the learning of complicated fault patterns by allowing information to propagate across multiple layers by utilising skip connections. We do trials on the MixedWM38 WaferMap dataset, which contains a variety of defect types, to assess the proposed technique. The outcomes show that when compared to cutting-edge techniques, the JAS-GCNN performs better. It successfully detects various faults and performs defect classification jobs with high accuracy.

Flaw Detection in the Deep-Rod Pump Cylinder after Ion Nitriding

The article is devoted to evaluation of ion nitriding influence on possible deformation of deep-rod pump cylinders made of 38X2MUA steel tube blank. This article describes the analysis of the main flaws in the samples of deep-rod pump cylinders, obtained by cylinder surface hardening by means of plasma nitriding. Several samples of deep-rod pump cylinders of were selected, subjected to machining in accordance with technological process, chemical and heat treatment of the internal working diameter of cylinders by ion nitriding. Manufactured samples were subjected to instrumental control on Aerotest «Cylinder WGN-R»of actual parameters of the inner cylinder diameter to the required by design documentation. Further, sample witnesses were made from the cylinders to test nitrited layer hardness and to make metallographic analysis of the nitrited layer microstructure. The study revealed deviations in the shape and dimensions of the inner cylinder diameters, which were classified into two types: hoop and oval strains. Metalographic and hardness tests results showed deviation in surface hardness and deviation from the nitrited layer normal microstructure. The structure of the nitrited layer of the strained specimens has large surface nitride inclusions and a loose nitrited layer, regardless of the flaw type. Also, defective samples lack the required high-quality nitride area of higher hardness, to provide high corrosion and tribotechnical properties determining the life-span of the deep-rod pump cylinder. In this way, the main types of flaws arising at hardening of the internal cylinder working diameter have been identified and classified. The microstructure of the nitrited layer in the standard and defective samples is investigated and described. The correlation between nitrited layer structure flaws and geometrical parameters of the inner cylinder diameter have been determined.

Modern Neural Network Technologies Text-to-Image

This paper discusses state-of-the-art graphical text-to-image neural networks and methods for text-to-image conversion, analyzing the results achieved and samples created to date for text-to-image conversion tasks. Ways of applying neural network approaches to text-to-image transformation for environmental monitoring, infrastructure and medical data analysis tasks are proposed. In this paper the results of neural network generation and its correlation with the user input linguistic constructions of text queries are reviewed, and the typical flaws and artifacts typical of the neural network generated images are identified and classified. The rapid development of neural network technologies in this field could have a significant impact on society, the professional market and the media, which makes the task of studying neural network images and identifying them among other graphic content particularly relevant.

Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder

Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal.

Experimental Investigation on the Failure Behavior of Single-Stiffened Composite Panels under Compressive Load

Whilst the thin-walled composite structures have been widely used to build the primary components in aircraft, Low-Velocity Impact (LVI) can cause severe intra-laminar and inter-laminar damage in the composite material, reducing the mechanical performance of these components. This paper presents an experimental study on the compressive failure behavior of a Single Hat-Stiffener Panel (SHSP) with two typical flaws, i.e., impact-induced damage and skin/stiffener debonding. LVI tests were performed to introduce the damage around the radius edge of the stiffener and the center of the flat skin panel, respectively, while the skin/stiffener debonding was introduced to the SHSP by artificially embedding a PTFE film at the skin/stiffener interface during autoclave curing. The impact energy to cause Barely Visible Impact Damage (BVID) was determined to be 22 J for the thin SHSP configuration. Whilst the considerable damage area was detected by the ultrasonic scan for the thick SHSP at an energy level of 35 J, the impact-induced indentation was not visually detectable (<0.35 mm), indicating a high impact resistance of this thick SHSP. Compression After Impact (CAI) results showed a significant reduction (59.1%) in the ultimate compressive strength of the SHSP when the impact location was on the stiffener compared to the pristine specimen. However, this reduction is less significant (34.7%) when the damage resulted from the same impact energy in the flat skin panel. In this study, the presence of skin/stiffener debonding does not adversely influence the compressive strength of the thick SHSP, while this debonding flaw appears to cause a minor reduction in the compressive strength of the thin SHSP.

Investigation of Typical Flaws of Grinding Balls Produced from Rejected Continuously Cast Rail Steel Billets

Investigation of Typical Flaws of Grinding Balls Produced from Rejected Continuously Cast Rail Steel Billets

Оценка применимости метода неразрушающего контроля наведенными низкочастотными вибрациями путем анализа преобразования энергии колебаний

There were described different types of flaws in various equipment. A new method of non-destructing testing (NDT), using induced low-frequency vibration was suggested. In order to examine the hypothesis, a row of experiments was conducted. There were tests, involving a steel slab (4 mm thick with 2 mm deep cut, imitating a crack).In result, amplitude-frequency correspondences were set up and motions of vibrational energy were figured out due to the occurrence and position of a flaw. Vibrational energy-frequency curves were set up. Analysisof the curves affords us to find out links between vibrational energy and flaw occurrence. Due to the resultsof the experiments a new NDT method was suggested, which could use low-frequency vibrations, seeking for a flaw.It could be effectively used in testing small-diameter pipes.

Automated visual inspection of manufactured parts using deep convolutional neural networks and transfer learning

Most manufacturing processes involve some form of visual quality control of the produced parts. Automated solutions can reduce the required manual work significantly while increasing reliability. However, common obstacles to the construction of smart visual inspection systems are the complexity of the inspected parts, varying types of defects, and small datasets. In this study, we apply state-of-the-art convolutional neural networks to classify infrared images of thermal conductive components manufactured in a real factory setting. Typically, training deep neural architectures requires very large datasets, but this effect is mitigated by using transfer learning. The dataset consists of 6,000 images with 4,200 defect samples and 1,800 intact samples, including different types of flaws and component models. We present a concept for implementing the automated visual inspection system, including dataset preparation, model training, and the inline application. The goal is to establish a Human-in-the-Loop approach, that maximizes accuracy and safety while keeping the required human work at a minimum. A key finding of our research is that dataset preparation and cleaning had a greater impact on the classification accuracy than the optimal choice of the model or training parameters.

Effect of internal lattice structure on the flexural strength of 3D printed hierarchical porous ultra-high temperature ceramic (ZrB2)

3D printing of technical ceramics using direct ink writing (DIW) of multiphase colloidal inks has the unique ability to create structures with hierarchical features. To facilitate the application of 3D printed hierarchical porous ultra-high temperature ceramics (UHTCs), additional limiting factors such as strength and the effect of 3D printed internal lattice structure need to be better understood. This study reports on the strength dependence of common DIW print parameters including internal lattice structure shape, nozzle diameter and spacings between adjacent filaments. The present study applies Weibull statistics to the experimental array that considers macro features introduced through print parameters as flaw types, which shows strength of 3D printed hierarchical ZrB2 is highly dependent on the introduced 3D printed structure, size and the stressed volume. This work provides essential information that can be used in the initial stages of design when considering implementation of additively manufactured hierarchical porous UHTCs.

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.