Automated identification and characterization of clustered weld defects

This study proposes an X-ray detection method based on Convolutional Neural Networks (CNN) for the identification of weld defects in industrial pressure vessels. Compared to the limitations of traditional machine learning methods (such as Support Vector Machines, Random Forests, and Wavelet algorithms) that rely on manual feature design, CNNs automatically extract multi-level features through deep learning, significantly improving detection efficiency and accuracy. By combining image preprocessing techniques, this method enhances image quality and feature discrimination ability. Experimental results show that in the detection of point defects, linear defects, and planar defects, the accuracy of CNN is significantly superior to the Wavelet algorithm, with a lower error rate, and it exhibits stronger robustness to noise and changes in lighting. This method effectively reduces dependence on human expertise and provides strong support for efficient and precise weld defect detection.

Complete nucleotide sequence of the region encompassing the first twenty-five exons of the human proα 1(I) collagen gene ( COL1A1)

Certain sewer deterioration modeling approaches, represent the condition of a pipe as the sum of individual defect severities, leading to the loss of spatial information about defects in the pipe. This paper proposes a hybrid framework for incorporating spatial information of defects into the analysis of sewer pipe condition. The first component of the framework is called Defect Cluster Analysis (DCA) and it seeks to identify defect clusters (i.e. areas with multiple defects in proximity) and quantify their severity. The second component of the framework is called Defect Co-Occurrence Mining and it attempts to identify groups of defects, which occur simultaneously in pipes. The framework was evaluated on data from 7193 inspections of sewers in the US. Validation of the results by subject matter experts indicates that the proposed framework enables a fine-grained analysis of pipe condition and could be instrumental in rehabilitation decision-making.

On the thermoelectric power variation according to temperature of zirconium alloys and its possible application to the estimation of the amount of solubility variations of iron and chromium in zircaloy

Control over individual point defects in solid-state systems is becoming increasingly important, not only for current semiconductor industries but also for next generation quantum information science and technologies. To realize the potential of these defects for scalable and high-performance quantum applications, precise placement of defects and defect clusters at the nanoscale is required, along with improved control over the nanoscale local environment to minimize decoherence. These requirements are met using scanned probe microscopy in silicon and III-V semiconductors, which suggests the extension to hosts for quantum point defects such as diamond, silicon carbide, and hexagonal boron nitride is feasible. Here we provide a perspective on the principal challenges toward this end, and new opportunities afforded by the integration of scanned probes with optical and magnetic resonance techniques.

Auger Electron Spectroscopy (AES) is one of the few techniques that has surface sensitivity and small analysis volume to make it the ideal analytical technique for the compositional characterization of submicron defects. However, the integration of defect inspections at only a few processing steps during device fabrication results in the detection of many buried defects. In order to identify these defects, it is necessary to determine their composition. Combined with Focused Ion Beam (FIB) technique to expose the cross section of the buried defect, Auger analysis provides accurate identification of buried defects that are critical for quickly ramping to higher yields and recovering from yield excursions. This paper reports two examples of the use of AES combined with FIB to diagnose processing problems.

Defects in transition metal dichalcogenides (TMDs) are pivotal in modulating their electronic, optical, and catalytic properties. Investigating these defects is essential for advancing both fundamental knowledge and practical applications. In this study, we examine the individual defects in a monolayer of MoTe2 supported on graphene grown on an Ir(111) substrate by means of scanning tunneling microscopy (STM). Charging rings appear at distinct bias voltages, revealing ionization levels of doping centers located either below or above the Fermi level. Although direct STM visualization of point defects was not achieved, the combination of scanning tunneling spectroscopy (STS) and density functional theory (DFT) calculations enabled the identification of the structural origins of the charge states. Our results demonstrate a powerful tool for uncovering the electronic and structural characteristics of atomic-scale defects in MoTe2, contributing to the understanding of defect-driven electronic properties in transition metal dichalcogenides.

Background/Objectives: Grain structure of Austenitic Stainless Steel (ASS) weld causes difficulty in defect detection while inspecting using Time of Flight Diffraction (TOFD) method. This study aims at comparing segmentation methods to overcome this difficulty based on defect characterization. Methods/Statistical Analysis: This study makes use of TOFD and Radiographic images of ASS weld pads fabricated with defined linear and volumetric defects. The Region-Based Level Set algorithm and Discontinuity Based Segmentation algorithm were explored for achieving flaw segmentation and quantitative characterization and validation of the result with that of standard radiographic results. Findings: The efficiency of the algorithms was analyzed by comparing and validating the size of defect with that of standard radiographic results in the form of error percentage. The consistency of error percentage in defect sizing (up to 11%) achieved by RegionBased Level Set algorithm for all the test images given in the database indicate that, this algorithm is the best as compared to Discontinuity Based Segmentation algorithm(error percentage up to 47%) for defect segmentation and characterization in TOFD images. Application/Improvements: The segmentation algorithm enables automation of measurement process and enhanced detection and characterization of defects at the initial stage. Further, it reduces human fatigue caused by operator while defect detection as the volume of data increases. The future direction is to fully automate the system in order to save time for interpretation and to modify the algorithm to segment images with multiple defects. Keywords: Defect Characterization, Discontinuity Based Segmentation, Flaw Segmentation, Region-Based Level Set Segmentation, Time of Flight Diffraction (TOFD), Weld Defect

Interfaces are planar metastable defects with singular features capable of controlling diverse material properties, including mechanical response and the microstructure evolution in materials under irradiation. This ability of interfaces to dictate the material response resides inherently in their atomic structure, which controls the interactions of dislocations as well as point and defect clusters with the interface. We recently showed how dislocations nucleated from defect clusters interact with a heterophase interface in Cu–Nb layered composites. We also showed how the ability of the interface to absorb vacancy clusters depends on the atomic structure at the interface. Herein, we elaborate on the effect of the atomic structure on the ability of the interface to absorb dislocations as well as vacancy and self-interstitial defect clusters. We study a physical-vapor-deposited Kurdjumov–Sachs orientation in a Cu–Nb interface and an asymmetric \(\Sigma \)11 grain boundary in pure Cu. On the one hand, the manner in which dislocations react with the interface depends on the misfit dislocation arrangement, which substantially differs between these two cases. On the other hand, vacancy and self-interstitial clusters are absorbed similarly upon interaction with both structures.

GeV ion irradiation of NiFe and NiCo: Insights from MD simulations and experiments

Pulse echo signals measured in ultrasonic NDT include the effect of the measuring systems, the propagation paths taken by the ultrasonic waves, and are corrupted by additive noise. For instance, the ultrasonic signals for a particular reflector recorded under the same conditions, but using different transducers can be quiet different. This leads to the difficulty of comparing and analyzing signals particularly in automated defect identification systems employing different transducers. Generally, it is assumed that the measured pulse echo is obtained by linearly convolving the defect impulse response with the measured system response. Deconvolution operation therefore, seeks to undo the effect of the convolution and extract the defect impulse response which is an essential step for the identification and characterization of defects. Conventional deconvolution techniques such as least square, Wiener filter, and minimum variance deconvolution [1] are based on a priori knowledge of second-order statistics (SOS) of the noise and the input signal. In practice however, ultrasonic pulse echo signals are found to be non-minimum phase systems and the acoustic noise due to scattering from the grains inside the propagation medium does not have a readily known statistic [2]. SOS-based deconvolution techniques, being phase-blind cannot therefore, accurately estimate the defect impulse response.

Optical absorption of polycrystalline corundum (polycor) after irradiation with iron ions and subsequent annealing in vacuum was studied. It was found that substitutional, intrinsic radiation, biographical defects, and complexes on their basis have an effect on the absorption parameters and optical transitions between localized states and allowed bands. The contributions to changes in the optical properties of individual substitutional defects, their clusters, and complexes with the participation of substitutional defects and radiation defects were singled out. The influence of defects and iron oxides formed upon annealing on absorption was estimated. The most proable nature of clusters of substitutional defects and impurity-vacancy complexes was identified.

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

Lattice defects in silicon rapidly solidified from the melt

The chromosomal etiology of congenital gonadal defects