Identification Of Imperfections Research Articles

Power of a test for assessing interlaboratory consensus of nominal and ordinal characteristics of a substance, material, or object

A concept of the consensus among different laboratories participating in an interlaboratory comparison, classifying a substance, material, or object according to its nominal and ordinal (i.e. categorical) characteristics, is devised using decomposition of the total variation of the laboratory responses. One of the components of the total variation is caused by the between-laboratory differences, and the second—by conditions associated with the applied experimental design (for example, temperature of test items, technician experience, etc). This decomposition is based on the recently developed two-way CATANOVA for nominal variables and two-way ORDANOVA for ordinal variables. The consensus is tested as hypotheses about homogeneity, i.e. insignificance of the corresponding components of the total variation. The consensus power is taken to be the power of the homogeneity test. A methodology for evaluation of the consensus power and corresponding risks of false decisions versus the dataset size of categorical characteristics obtained in an interlaboratory comparison is detailed. Examples of evaluation of the power and risks are discussed using previously-published datasets of an interlaboratory comparison of identification of weld imperfections, and an examination of the intensity of the odor of drinking water. An example of computer code in the R programming environment is presented for the power calculations in the case of nominal variables, using a chi-square distribution. A newly developed tool for ordinal variables, an Excel spreadsheet with macros, which is based on Monte Carlo draws from a multinomial distribution, is also available.

CRGF-YOLO: An Optimized Multi-Scale Feature Fusion Model Based on YOLOv5 for Detection of Steel Surface Defects

The identification of imperfections on steel surfaces is vital for ensuring the quality of industrial products. It requires the capability of real-time detection with high accuracy. This paper proposes the CRGF-YOLO (Contextual Reparameterized Generalized Feature) model based on YOLOv5. In the network, BottleneckCSP structures and depthwise separable convolutions utilizing the structural reparameterization are introduced to reduce the model size and improve performance. In addition, contextual transformer modules are employed as self-attention mechanisms to improve feature representations by capturing long-range dependencies, outperforming conventional convolutional networks. Furthermore, the simplified generalized feature pyramid network is embedded to aggregate multi-scale feature maps and enhance the network’s robustness. Finally, four prediction heads with different sizes are employed to predict defects, which are supported by prior bounding boxes generated using k-means clustering algorithm. The Focal-EIOU (Exponential Intersection over Union) loss function is introduced to improve detection accuracy and expedite model convergence. The improved model achieves a mean average precision (mAP) of 82.2% on the NEU-DET dataset, outperforming the baseline YOLOv5s by 7.7% mAP while maintaining real-time speeds. Comparative evaluations demonstrate CRGF-YOLO’s superior performance over previous state-of-the-art methods like Faster R-CNN (77.4% mAP), YOLOv3 (77.4% mAP), YOLOv7s (72.1% mAP), and YOLOv8s (78.7% mAP) for steel surface defect detection. Overall, this study provides valuable insights and practical guidance for the advancement of defect detection technology.

Lightweight strip steel defect detection algorithm based on improved YOLOv7

The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data mining. This paper is targeted at three aspects: the structural and semantic exploration of repositories, deriving characteristic features in value and time perspectives, and defining the space of project monitoring goals. The considerations presented demonstrate a holistic image of the project development process, which is useful in the assessment of its efficiency and identification of imperfections. The original analysis introduced in this work was verified using open source and some commercial software project repositories.

Real-time defect detection using online learning for laser metal deposition

Defect prevention and detection are very crucial for the quality improvement of additive manufacturing (AM) processes. Timely identification of imperfections and flaws in the manufactured products will allow effective and early implementation of corrective actions and thus impede the spread of defects to the whole industrial value chain. In laser metal deposition (LMD) AM processes, defects are imperfections which typically include “porosity” and “cracks formation”. This study presents novel data-driven anomaly detection techniques that use both transfer learning and online learning to assess the quality of melt pool images taken during the LMD process of a part. The proposed methods incorporate the characteristics and dynamics of the manufactured part during the printing process along with previous knowledge acquired from historical data. Continual online learning models (K-means and self-organizing maps) are developed whose parameters are adapted to the incoming data collected in real-time as a new part is being manufactured. The proposed models significantly outperformed the performance of their batch-learning counterparts in detecting anomalous melt pool images in an additively manufactured Ti–6Al–4V thin-walled part. Both models required an average of ∼0.07 s to process each incoming melt pool image, update their parameters and give a prediction on the image's health. This shows the potential of continual online learning for real-time anomaly detection.

Problematic aspects of terminology for sugary confectionery products

The article is devoted to the study of terminology and the identifi cation of imperfections and errors in terminology for sugary confectionery products in various regulatory documents. The necessity of harmonization of terms enshrined in regulatory documents of various levels, such as interstate and national standards for sugar confectionery, as well as the EAEU Commodity Nomenclature of Foreign Economic Activity and explanations to it, is shown.

Towards an automatized and objective assessment of data from visual inspections of building envelopes

The renovation planning process is filled with uncertainties and subjective decisions. These make the decisions upon what and when to renovate a complex and ambiguous problem. Selection of renovation measures related to building envelope are often far from optimal as decisions are usually made based on visual inspections. These are manned and thus prone to subjective assessment and the knowhow of individual inspectors. Furthermore, objective criteria which could indicate non-structural failures are often missing. The objective based planning process allowing the estimation of the current damage status of the building envelope by only using non-destructive measurements is still in its infancy. The first step requires establishing reliable and objective based data collection. These could be efficiently collected by Unmanned Aerial Vehicles (UAV) with subsequent image recognition algorithms allowing the identification of imperfections and store the position and extent of such deviations into the building’s digital assessment database. Such tools do not exist. The aim of this study is to investigate the current objectivization possibilities in the domain of building inspections. The first part provides a literature review describing how an autonomous UAV survey of a building envelope may be planned and what computer vision techniques may be used for automatic damage recognition and classification. Subsequently, an objective detection model based on the YOLO-tiny (You Only Look Once) computer vision framework is employed in a case study investigating a building envelope of historical Tjolöholm castle in Sweden. This study contributes to developing a methodology for an objective based visual inspection process.

Additively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures

Additive manufacturing overcomes the restrictions of classical manufacturing methods and enables the production of near-net-shaped, complex geometries. In that context, lattice structures are of high interest due to their superior weight reduction potential. AlSi10Mg is a well-known alloy for additive manufacturing and well suited for such applications due to its high strength to material density ratio. It has been selected in this study for producing bulk material and complex geometries of a strut-based lattice type (rhombic dodecahedron). A detailed characterisation of as-built and heat-treated specimens has been conducted including microstructural analyses, identification of imperfections and rigorous mechanical testing under different load conditions. An isotropic elastic–plastic material model is deduced on the basis of tension test results of bulk material test specimens. Performed experiments under compression, shear, torsion and tension load are compared to their virtual equivalents. With the help of numerical modelling, the overall structural behaviour was simulated using the detailed lattice geometry and was successfully predicted by the presented numerical models. The discussion of the limits of this approach aims to evaluate the potential of the numerical assessment in the modelling of the properties for novel lightweight structures.

A Robotized Non-destructive Quality Device for the Inspection of Glue Joints by Active Thermography

Due to its flexibility for use in various applications active thermography is of interest for non-destructive evaluation of defects in various materials. In particular, active thermography allows for identification of imperfections inside glue joints like delaminations, air pockets or inclusions of foreign particles. The contribution of this work is the development and validation of a small and portable device that can inspect large glue joints of fiber composites components like aircraft fuselages and wind turbine blades. The thermography device consists of a heat source and an infrared camera mounted to a mobile industrial robot system that guides the device along a trajectory above the inspection object. At the same time, images are acquired by the infrared camera and the current position of the device is monitored. The images are processed via LabVIEW to automatically detect irregularities and save the coordinates and an image of the defect in a log. This enables tracking, closer analysis and eventual repairs. Compared to previous thermography procedures this method allows downsizing of the heat source and flexible adjustments to the size and contour of the test component. As a result, it is possible to reduce testing times and divide the complete surface into small areas which allows detection of sub-millimeter defects during the manufacturing process and for example inspection of wind turbines already in operation. Therefore, this device is a powerful tool for quality management in the industry 4.0.

Numerical analysis of the dynamic frequency responses of damaged micro-lattice core sandwich plates

In this work, a numerical analysis of the dynamic frequency responses of sandwich plates with micro-lattice core is presented. The finite element analysis is implemented in the commercial software Abaqus/Standard, calculating the natural frequencies and eigenmodes of sandwich plates containing defects on the micro-lattice structure. In order to include the presence of defects, an aleatory algorithm is developed in MATLAB. The effect of the damage percentage, cell type, cell size and material on the frequency and modal responses of the sandwich plates is highlighted. Results show that the dynamic frequency response may be useful for analysing practical issues related to non-destructive damage identification of imperfections in the micro-lattice core of sandwich structures.

Quality of welded butt splices in railway composite bridges on the Central Trunk Railway Line

Radiological tests were carried out on steel girders of composite bridges located on the Central Trunk Railway Line (CMK) in the years 1985-1986 according to the technical program of Transportation Ministry MK-133-06-02-04. The paper presented the test results obtained on seven composite steel-concrete railway structures. The aim of the tests was the identification of welding imperfections (WI) in butt splices of main girders and influence on their condition after the ten-year service. The test results may be useful for the technical assessment of the bridges associated with the adjustment to speed of 300 km/h. The tests confirmed the good quality of welds in bridge structures under consideration.

Identification of imperfections by means of dynamic response

The idea of using the changes of the characteristics of natural or forced vibrations (natural frequency, natural modes, damping) for the determination of the position and magnitude of damage to building structures originated in the 70s [1]. The achievement of ever higher accuracy of developing dynamic displacement sensors and evaluation instruments has made this idea increasingly applicable. Identification is a process defining the properties of a structure and the characteristics of its design model. The often required identification task of determining the rate of deterioration of structures by means of their dynamic response has been numerous lately. The paper sums up the experience of the Institute of Theoretical and Applied Mechanics (ITAM) of the Czech Academy of Sciences, with laboratory tests of large models and in-situ measurements.

Identification of imperfections in thin plates based on the modified potential energy principle

A procedure to identify the imperfection in thin plates is proposed in this paper. The modified potential energy principle, which serves as the theoretical basis of the identification procedure, is improved to allow for the experimental measurements in static tests. Several typical examples are studied to illustrate the effectiveness of the procedure.

Geometrical and Material Characterization of Quenched and Self-Tempered Steel Reinforcement Bars

Quenched and self-tempered (QST) steel reinforcement bars (rebars) are manufactured by Thermex or Tempcore processes. Characterization studies of QST rebars are mostly limited to reveal the hardened outer layer and some improved mechanical properties compared to hot-rolled (HR) and cold-worked (CW) rebars. However, investigations on residual stresses and imperfections originating from the manufacturing process as well as stress concentrations arising from the ribbed profile are rarely found in the literature. Surface residual stress may be beneficial or detrimental to the fatigue performance of rebars although they have been studied only on the subsurface of QST rebars. Surface imperfections are zones of stress concentration from where fatigue cracks may initiate. Imperfections are usually identified in the fractured cross section resultant from fatigue tests of rebars. Stress concentrations can also arise from the rib geometry. Although the rib geometric parameters affect the stress concentration factor Kt on rebars, stress concentration analysis are restricted to two-dimensional (2D) finite-element models (FEMs). The study presented herein is part of a further detailed investigation on the fatigue behavior of HR-CW and QST rebars in the very high cycle domain. In the present work, characterization analyses of QST rebars include (1) experimental investigation of surface and subsurface residual stresses on QST rebars by cut compliance and X-ray diffraction techniques (residual stresses from both techniques are discussed); (2) identification of surface imperfections by scanning electron microscopy (SEM) analysis; and (3) three-dimensional (3D) finite-element analysis of stress concentrations on the ribbed profile. The influence of the rib geometry such as radius, width, height, and inclination as well as the rebar diameter on Kt values are analyzed. The critical zones are determined along the ribs. (C) 2016 American Society of Civil Engineers.

Rejection of Welded Connections According to EN ISO 10675: a New Approach to Increasing Objectivity

The currently applied criteria of identification of imperfections based on results of Radiographic Testing have no unambiguous description in regulatory documents. Therefore, there is a lack of coherence between opinions of the experts who are interpreting images on radiographic films .Authors suggest dividing images of imperfections into classes, each of which includes imperfections with similar projections on radiographs. This allows to simplify the identification procedure and to increase repeatability of results of the interpretation which is carried out by different experts independently.

Identifying Roadway Surface Disruptions Based on Accelerometer Patterns

In developed countries large-scale technology is used for the monitoring and management of road infrastructure. Sensor networks together with surveillance cameras help identify elements disturbing cars' mobility. On the contrary, in developing countries, the lack of this level of infrastructure makes the task of roads' maintenance and traffic control challenging. The presence of roadway surface disruptions (RSDs), in particular, affects the economy and reputation of individuals and companies. Urban computing strategies can be adopted to collect data about the presence of RSDs and be applied to monitor traffic related issues. The implementation of such level of navigation assistance could help save freight and money to companies and authorities, and even reduce cars' accidents. In this direction, current mobile technologies can help with the identification and location of road imperfections alerting drivers about alternative routes. In this paper we present our work that seeks to enable citizens' cars as road watchers. By means of the mobile phones' acceleration sensing capabilities we are identifying and tagging the presence of RSDs. Using Android-based devices situated on the copilot floor side of a car, 5 Mbytes of road information has been collected. We run a series of experiments aiming to differentiate acceleration patterns associated to potholes, speed bumps, metal humps and rough roads. Currently, the classification of disruptions is being experimented with techniques from the field of Machine Learning (ML) such as artificial neuronal networks and logistic regression. Classification of individual events is over 86% of accuracy that is competitive with those reported in the literature. In this work we provide the first public dataset that could be used by other researchers to offer more insight in this problem.

Stochastic identification of imperfections in a submerged shell structure

Accurate predictions of the buckling load in imperfection sensitive shell structures requires precise knowledge of the location and magnitude of any geometric imperfections in the shell (e.g. dents). This work describes a non-contact approach to identifying such imperfections in a submerged shell structure. By monitoring the acoustic pressure field at discrete points proximal to a shell structure excited by a cyclic membrane (i.e. in-plane) loading, it is noticed that parameters, describing small scale denting, can be identified. In order to perform the identification, a fluid-structure model that predicts the spatio-temporal pressure field is required. This model is described in detail and includes the predicted effects of the imperfection on the observations. A Bayesian, Markov chain Monte Carlo approach is then used to generate the imperfection parameter estimates and quantify the uncertainty in those estimates. Additionally: for cases involving the occurrence of an unknown number of dents, reversible jump Markov chain Monte Carlo (RJMCMC) methods are employed in this work.

Application of Laser Microscope System for Evaluation of Separation Agents

This paper demonstrates application of laser confocal scanning microscope system for fair-face concrete surface evaluation. Measure system presents a new generation optical system for identification and description of esthetical imperfections of various building materials. System enables 3D observation and high-precision 3D measurement in real time.

Investigation of Electric Properties of Nanocomposites in Frequency Domain - Compact Measurement Data Representation

The paper presents a procedure for mapping large amounts of experimental measurement data in a compact and representable form, preserving information on the properties of the research object for further investigations. The raw data set is compacted using mathematical description of dielectric relaxation. This approach provides a possibility to recover the dielectric properties for any frequency value, even extrapolating outside the actual measurement range. It also favours the understanding the causes for the measured phenomena and identification of imperfections within the measurement procedure.

Stochastic inverse identification of geometric imperfections in shell structures

It is now widely known that the presence of geometric imperfections in shell structures constitutes an important contribution to the discrepancy between theoretical and experimentally realizable ultimate loads governed by buckling. The present paper describes a method by which an actual initial imperfection field may be estimated using the service load response of a shell structure. The approach requires solving a stochastic inverse problem wherein uncertainty regarding initial imperfection predictions is expressed within the context of a Bayesian posterior distribution. The proposed approach could be applied to condition assessment and performance evaluation activities in practice.