Reliable Inspection Research Articles

Automatic monitoring system designed to detect defects in PET preforms

The goal of this work is to automate the defect detection system for PET preforms production. For this purpose, it is necessary to consider the machine vision method, which has hardware and software structures that include many technical components. The software in turn includes two parts: one is used in the computer for image processing and the other for controlling the mechanical components of the system. However, this is a very expensive and time-consuming process due to the collection of large amounts of information with labeled defect samples. As shown, this technology can improve the scope, efficiency, quality and reliability of industrial inspection, which in turn leads to a number of advances in modern industry. Also, the company is able to increase its productivity, reduce the cost of defect controllers’ salaries, increase profits, and avoid creating situations in which equipment will be idle.

Visual testing: the influence of selected human factors on probability of detection

To ensure the reliable assessment of components by visual inspection, current standards overemphasise the focus on illuminance as being the only factor of influence. Human factors or an adaptation of viewing conditions to different inspection tasks are not sufficiently considered. In previous investigations, a significant influence of illuminance, testing forged parts with highly reflective surfaces at three different illuminance levels (200 lx, 350 lx and 500 lx), could not be proven. Instead, a more complex system of different influencing factors was indicated. In this work, groups with different human characteristics were compared to investigate the correlations in indication detectability during visual inspection. A questionnaire was used to record various subjectively perceived factors (dissatisfaction, general and lighting scenariospecific disturbance) in the detection of crack-like indications, under three different lighting scenarios. From the factors studied, age, general and specific experience of the participants were found to be the most relevant. General experience in the field of visual inspection, measured in years and frequency of inspections performed in practice, was shown not to have a decisive influence. On the other hand, the influence of specific experience with the test specimen, or comparable components, and the age of the participants seem to have an influence. With increasing age, the probability of detection seems to decrease, while it seems to increase with rising component-specific experience. In the interplay of the factors investigated, specific experience can be influenced most effectively and could thus be increased by intensified and specific practical training. This could have a positive effect on the reliability of visual inspection.

Data fusion for improved circularity through higher quality of prediction and increased reliability of inspection

Data fusion for improved circularity through higher quality of prediction and increased reliability of inspection

CNN 기반 선박 형광 도막 두께 측정

To reduce the number of ship painting inspections in shipyards, there are trials to use visible fluorescent paint with a thickness of paint that can be visually inspected. However, due to the problem that the paint color varies depending on the illuminance and the type of light source, the reliability of the visual inspection is not consistent depending on the inspectors. Therefore, this study proposes a painting inspection method using machine learning technique instead of visual inspection. We propose automation of paint measurements using CNN model to find color variations in captured images according to the illuminance of paint. The actual thickness value of the paint was obtained from the specimen using a contact thickness measuring device. The color model was used to create a deep learning model suitable for the thickness characteristics of the image data. As a result, the proposed CNN model can measure the thickness of the paint within ±20 μm.

Artificial Intelligence (AI)-Based Evaluation of Bolt Loosening Using Vibro-Acoustic Modulation (VAM) Features from a Combination of Simulation and Experiments

The detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave–surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences of structural and material nonlinearities. A reliable inspection method of residual bolt torque is proposed in this study using support vector regression (SVR) with acoustic features from VAM. By considering the material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), the responses of aluminum–aluminum and composite–composite bolted joints during the VAM test were accurately simulated. The SVRs were subsequently established based on the database built by combining simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses had higher accuracy compared to the conventional nonlinear coefficients. Requiring limited experimental data, the proposed method can achieve a reliable inspection of bolt torque by including the simulated data in the machine training.

Fast and high-resolution laser-ultrasonic imaging for visualizing subsurface defects in additive manufacturing components

Additive manufacturing (AM) is an emerging technique for efficient fabrication of individually tailored and complex geometry parts. The fabrication process is prone to induce various defects that can have detrimental effects on the AM components. Therefore, a reliable technique that enables monitoring the integrity of AM components and in return helping to optimize the fabrication parameters in mission-critical structures is highly demanded. This work presents a fast and high-resolution damage visualization method using laser-ultrasonic (LU) imaging technique for accurately detecting and quantifying the subsurface defects in printed AM components. Specifically, a fully noncontact LU scanning system is implemented to generate and detect high signal-to-noise ratio laser ultrasonic waves using a pulsed laser and laser Doppler vibrometer, respectively. A strategy for fast defect localization using Rayleigh waves with circular scans is firstly proposed. The high-resolution 3D synthetic aperture focusing technique (SAFT) imaging with raster scans is subsequently performed focusing around the located damage areas to stereoscopically visualize and quantify the subsurface defects. The reconstructed images are further processed and improved using Gaussian filter algorithm to obtain accurate defect shapes, sizes, and positions. The feasibility of the proposed method is eventually verified on AlSi10Mg and stainless steel (316L) components containing subsurface defects with various types and dimensions. The measured sizes are well consistent with the designed values, suggesting that it is a reliable inspection method for AM parts to ensure quality control and feedback.

Risk-based maintenance optimization of aircraft gas turbine engine component

The integrity of an aircraft gas turbine engine is critical for safety of flight. Although the reliability of engines has improved considerably, a large number of legacy engines continue to operate. The maintenance of legacy engines is a major burden for their operators owing to the high cost involved, and the engines pose a high risk to flight safety. In this study, we developed a comprehensive approach for risk-based maintenance optimization of aircraft engine components. The approach involved the use of a physics-informed data-driven model incorporated with the Weibayes model and a simple fatigue crack growth model. The crack length distribution and corresponding risks were evaluated using Bayesian updating and knowledge of nondestructive inspection reliability. The model was used for the computation of the fatigue reliability of the first-stage blisk of a CT7-9C turboprop engine. A single failure was used for the Weibayes analysis, and a master crack growth curve was obtained through quantitative-fractography-based crack growth analysis. Furthermore, an inspection model based on the evaluation of field inspectors was used to obtain the posterior crack length distribution, and through a sensitivity analysis, important factors were identified. Finally, optimal inspection and replacement plans were formulated using approximated objective and constraint functions. In particular, the life-cycle cost was minimized while maintaining the risks within strict limits.

Selective simultaneous generation of distinct unidirectional wave modes in different directions using dual-array transducer

Shear horizontal (SH) waves are widely used in the non-destructive evaluation and structural health monitoring fields. Conventional periodic permanent magnet electromagnetic acoustic transducers (PPM EMAT) can generate a single SH wave mode, which propagates in both forward and backward directions. Generation in a single direction is usually preferred to ease signal interpretation and increase inspection reliability. Recently, we have proved that a single, either fundamental or high-order, SH wave mode can be unidirectionally generated using a dual PPM EMAT, with carefully designed driving signals. Distinct SH modes present different applicability due to their particular response to features in the structure, such as defects. Hence, unidirectional generation of two different SH modes simultaneously with a dual-array transducer is investigated in this paper. The selectivity of wave modes and the selectivity of the propagation direction are studied by combining pure wave mode excitability with the superposition of optimal excitation signals for unidirectional generation, addressed in the frequency–wavenumber domain. Excitation signals for optimal and simultaneous generation of the SH0 and SH1 modes propagating in the same direction or in opposite directions have been designed. Experiments on an aluminium plate have demonstrated that the two wave modes can be effectively simultaneously generated with only one dual-array transducer and the propagation direction of each wave mode can be flexibly controlled using the designed excitation signals.

Better assessment of neonatal jaundice at home (BEAT Jaundice @home): protocol for a prospective, multicentre diagnostic study

IntroductionSevere neonatal hyperbilirubinaemia can place a neonate at risk for acute bilirubin encephalopathy and kernicterus spectrum disorder. Early diagnosis is essential to prevent these deleterious sequelae. Currently, screening by visual...

Visual Inspection for Lower Limb Malalignment Diagnosis Is Unreliable.

Visual inspection of the lower limb is often part of standard clinical practice during a physical examination at the outpatient clinic. This study aims to investigate how reliable visual inspections are in terms of detecting lower limb malalignments without additional tools and physical examinations. This study enrolled 50 patients. Each patient underwent a whole leg radiograph (WLR); in addition, a standardized digital photograph was taken of the lower limbs. Four persons (different experience levels) visually rated the digital photograph twice (unaware of the hip knee angle [HKA] on the WLR) and placed them in the category: severe valgus (>5°); moderate valgus (2°-5°); neutral, moderate varus (2°-5°); and severe varus (>5°). Visual ratings were compared with the measured HKA on WLRs for correlation using Spearman's rho. Linear ordinal regression models with significance when P < 0.05 were used to test whether body mass index (BMI), age, gender, and HKA were possible risk factors for incorrect visual HKA assessment. Spearman's rho between the visual assessment and measured HKA on the WLR was moderate with 0.478 (P < 0.01). Women had an increased odds ratio of 3.7 (P = 0.001) for incorrect visual assessment. Higher HKA also increased the odds ratio for erroneous visual assessment with 1.4 (P = 0.003). BMI and age did not significantly increase the odds of erroneous visual leg axis assessments in this study. Visual assessment of the lower limb alignment does not provide clinically relevant information. Lower limb malalignment diagnoses cannot be performed using only a visual inspection. Physical examination tests and radiographical assessments are advised. Diagnostic level II.

Integrating ultrasound and x-ray computed tomography to evaluate the microstructure of binder jet stainless steel 316L components

Binder jet printing (BJP) is a promising additive manufacturing method with benefits in sustainability, material selection, and geometric design freedom. However, issues related to part quality persist, necessitating reliable inspection and characterization strategies. Traditional protocols involving sectioning and extensive sample preparation may miss crucial information about a components’ microstructure due to volumetric variations. This study explores ultrasonic inspection of binder jet SS316L tensile specimens containing spatially varying grain size and porosity by measurement of longitudinal wave speed and attenuation. The ability of ultrasound to detect porosity is evaluated by cross referencing wave speed and attenuation data with porosity data gathered from x-ray computed tomography (XCT). Three-dimensional pore volumes were collapsed into two-dimensional maps such that ultrasound and XCT could be compared in a point-by-point fashion. After tensile testing, the location of failure was compared against wave speed and attenuation extremes. The results show the potential of ultrasound as well as important considerations related to the inspection of additively manufactured parts with complex microstructures.

Nondestructive analysis of corrosion in ageing hardened AA6351 aluminium alloys

Aluminum alloys have been chosen over other lightweight structural materials because of their advantages regarding strength, long-term durability, and low cost. Aluminum comes under the category of highly reactive metals, where an inherently stable oxide layer can protect against aggressive corrosive conditions. Therefore, this research investigated the application of the ultrasonic nondestructive technique for inspection and monitoring of corrosion behavior of AA6351 heat-treated aluminum alloys. The samples were artificial age hardened at five different conditions. The complementary engineering experiments for materials evaluation included hardness, X-ray diffraction and salt spray tests. Overall findings showed correlations between ultrasonic and destructive data representing a fast, versatile and reliable inspection technique. Furthermore, findings show a significant correlation (>99%), thus supporting the benefits of the multiparametric ultrasonic test for heat treatment inspection and quality control of lightweight structural materials.

Improving the reliability of a visual inspection of damaged aircraft structural components made of composite materials

The given article represents the study of the influence of color, surface finish and shape of dents on the reliability of 3D surface dents visual inspection, which are formed due to damage to epoxy composite materials reinforced with carbon fiber resulted from impacts. This article provides an analysis of the influence of surface color of aircraft structural components made of composite materials on the reliability of a visual inspection. The test results are given. Using these values, it is possible to determine the cross-section profiles of surface defects caused by impacts with energy within the range from 5 J to 80 J. The new designs of aircraft, which have been put into service thus far, feature 50 % and more composite materials of the airframe mass and use monolithic carbon fiber composite panels for the fuselage skin. Carbon fiber composite is particularly sensitive to the post-impact compressive strength reduction, and the operating aircraft environment is characterized by an array of sources of impact damages. Samples of the surface appearance of real composite structures of the aircraft on impact is the confidential information. Currently available literature concerning impact damage to composite materials, focuses on impact testing using hemispherical impact elements of typical diameters Ø 15mm, Ø 20 mm or Ø 25 mm. Testing information regarding larger diameter samples is not provided. There is no published research into impact damages to monolithic, fully finished carbon fiber composites.

X-ray technology in civil engineering

X-ray has the unique ability to reveal what inside a structure and interpret it as a human readable image. This key feature makes X-ray imaging to a preferred inspection method for quality control across everything from food, micro electronics to heavy steel constructions. The newest technology developments towards digital X-ray imaging allow for fast and reliable inspection, addressing the high requirements of steel and concrete inspection. In civil engineering X-ray technology is used for weld seem inspection within construction of pipelines, bridges petrol-chemical plants and nuclear facilities. Quality assurance of steel constructions: X-ray inspection of pipelines and other steel structures are not only done during construction where weld seem inspection is one of the main applications for X-ray but also during maintenance where inspection for hidden corrosion and erosion to identify weaknesses before a breakdown occur. Damage assessment: Another important application is assessment of identified damages, as evaluation of identified cracks in steel constructions. With digital imaging it possible to get an immediate inspection result with high sensitivity and resolution. Inspection of concrete: A new emerging application is X-ray inspection of concrete constructions with X-ray. A modern 300kV portable X-ray system combined with digital image system can be used to inspect up to 20 – 25 cm concrete. With X-ray inspection of concrete, it is possible to identify and evaluate the condition of the reinforcement inside the concrete which is pivotal for the strength of the construction. It is also possible to identify the exact position of electrical installations or other features hidden inside the concrete which can prevent unexpected incidents related to drilling. The latest X-ray technology addresses todays and future challenges of quality control in civil engineering.

First insights into the human-related risks of tunnel inspection

Whereas human factors in the non-destructive testing (NDT) of metallic components are a poorly investigated topic (in comparison to other industrial fields such as aviation), human factors in the inspection of concrete components are even less known. Studies have shown that there is always some variability between individuals in their inspection results and that human factors affect the reliability of NDT inspections. And even though those human factors (the effects of and interaction between technology, organisation, environment, and individual characteristics) do not necessarily lead to negative inspection outcomes, their understanding is a vital step towards preventing possible structure-breaking failures and thereby ensuring the safety of industry, environment and infrastructure. To identify the possible human-related risks in the tunnel inspection, a systematic approach to risk management has been adopted. This involves identification of risks, their characterisation, and suggestions for risk treatment. In line with this approach, the inspections will be observed followed by interviews with inspectors to collect initial information about the inspections in the field and possible performance shaping factors. Furthermore, a human-oriented Failure Modes and Effects Analysis (FMEA) will be used to identify possible human-related risks throughout data collection and evaluation and to evaluate them with respect to their possible causes, consequences, the probability of their occurrence and with respect to existing and possible preventive measures. The results aim to increase our overall understanding of human factors related to NDT, provide first insights and understanding of human factors in tunnel inspection and suggest measures to prevent human error in that application.

Application of simulation software for NDT for civil engineering

Simulation is a powerful means in the design of inspection methods. It minimizes the number of iterations when building probe prototypes, optimizing inspection techniques and defining the test procedures. It is known to be valuable in supporting performance and reliability demonstrations requiring a lot of data (e.g., POD studies and qualification campaigns), where simulation can reduce the number of necessary mock-ups and experimental trials. Largely used in different industrial sectors, there is still great potential for NDT modelling remaining in the civil engineering field. The aging infrastructures, and the increasing needs for reliable evaluations of structural integrity require deeper investigations and developments, in which modelling can be of great help. The CIVA platform is a well-established multi technique simulation and analysis software in NDT. Developed by CEA LIST, but also resulting from the contribution of numerous industrial and academic partners within Europe, it implements concepts of numerical efficiency, imaging, and reliability demonstration. The range of NDT methods in CIVA includes UT, GWT, ET, RT, CT, Thermography and is extended by Structural Health Monitoring applications based on guided ultrasonic waves. In addition to physical models dedicated to metallic inspections largely used in the industry, CIVA offers the capacity to simulate ultrasonic inspections in other media such as concrete. It has recently included the definition of probes polarized to generate shear waves at 0°, thus capturing the current state-of-the art for concrete inspections. This contributes significantly to the potential for studies and inspection planning in the field of concrete inspections, which helps tremendously in ensuring reliable inspections. While such studies would require enormous computational effort for most other simulation techniques, the semi-analytical approach of CIVA makes it highly efficient and application-oriented. This paper illustrates models available in the ultrasonic module of CIVA and best practices for application cases in civil engineering.

Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model

To guarantee the safety of the power grid system, it is essential to proceed reliable powerline inspection. Insulators are key devices in the powerlines. Their major function is to achieve mechanical fixing and electrical insulation, they play a key role in power lines. Insulators are deployed outdoors. Therefore, ensuring the safe operation of insulators is significant in the powerline inspection. Among all the inspection method, visual inspection is the key way. However, problems such as large changes in outdoor lighting have a strong impact on the accuracy of insulator detection. To overcome the shortcomings of uneven illumination, low contrast and poor details display in outdoor images, in this paper we introduces an image enhancement method based on illumination correction and compensation. First, the input data is converted from RGB color space to the HSV space, and three components, H, S and V, are obtained. The saturation component S is enhanced adaptively, and the brightness component V is processed by multi-scale gradient domain guided filter (MGDGF). Then the illumination component of the image is extracted, and corrected by two-dimensional adaptive Gamma transformation. The new brightness component is fused by Retinex based models. It helps to enhance the dark details and overall brightness of the image. This method not only solves the uneven illumination problem of the image, but also improves the contrast and details, while maintaining the original naturalness. Further, we introduce a real-time one step detection model based on YOLOv5, to detect the defect of the insulator. We evaluate the proposed method on an open public dataset. The evaluation results demonstrate that our proposed method can get very competitive results while maintaining real-time performance.

Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models

Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority of these models are powerful at most in achieving the task of classification, with primitive detection of the crack location. On the other hand, several state-of-the-art studies have proven that pixel-level crack segmentation can powerfully locate objects in images for more accurate and reasonable classification. In order to realize pixel-level deep crack segmentation in images of historical buildings, this paper proposes an automated deep crack segmentation approach designed based on an exhaustive investigation of several U-Net deep learning network architectures. The utilization of pixel-level crack segmentation with U-Net deep learning ensures the identification of pixels that are important for the decision of image classification. Moreover, the proposed approach employs the deep learned features extracted by the U-Net deep learning model to precisely describe crack characteristics for better pixel-level crack segmentation. A primary image dataset of various crack types and severity is collected from historical building surfaces and used for training and evaluating the performance of the proposed approach. Three variants of the U-Net convolutional network architecture are considered for the deep pixel-level segmentation of different types of cracks on historical surfaces. Promising results of the proposed approach using the U2−Net deep learning model are obtained, with a Dice score and mean Intersection over Union (mIoU) of 71.09% and 78.38% achieved, respectively, at the pixel level. Conclusively, the significance of this work is the investigation of the impact of utilizing pixel-level deep crack segmentation, supported by deep learned features, through adopting variants of the U-Net deep learning model for crack detection on historical surfaces.

Simulation based compensation techniques to minimize distortion of thin-walled monolithic aluminum parts due to residual stresses

Thin-walled milled monolithic aluminum workpieces are common in the aerospace industry, because of their advantages such as favorable material properties, established design methods, good machinability, known performance and reliable inspection techniques. Machining-induced residual stresses (MIRS) and initial bulk residual stresses (IBRS) are known to be the key factor for causing distortion of those workpieces. In this study the effect of both types, the milling path strategy and the wall thickness on the distortion is analyzed. A developed 3D linear elastic finite element (FEM) model, considering both types of RS as input, and predicting the resulting part distortion, is extended by using the true milling path as well as all RS contained in the entire workpiece. The simulation outcome is validated by experiments and distortion minimization techniques are derived. The results show that the model predicts the distortion shape and level for different wall thicknesses and milling strategies with a high accuracy. The experiments indicated that the distortion of low IBRS parts with 3 mm wall-thickness could be minimized by 42% by only changing the milling path. The simulation highlighted, that for smaller wall thicknesses, the distortion minimization potential is even higher (e.g. 2 mm wall thickness: up to 69% distortion minimization). For high IBRS parts, milling the inverse form of the predicted shape on the backside of the workpiece represents an alternative, which led up to a reduction of the distortion by 77%. In general, three main categories for precontrol distortion techniques can be identified as the process parameters, the part topology and the process strategy.

Ultrasonic and phased-array inspection in titanium-based alloys: A review

Ultrasonic testing procedures are among the most extensively used techniques in non-destructive testing and non-destructive evaluation systems. Meanwhile, phased array inspection or phased-array ultrasonic testing techniques as a well-known type of ultrasonic testing in advanced non-destructive testing systems are utilized in numerous applications, including oil and gas, medical imaging applications, railway, automotive, marine, and aviation industries. Phased-array ultrasonic testing-based methods can also be used in a variety of weld inspections, bond testing, thickness profiling, flaw detections, corrosion inspections, and in-service crack detection. The rapidity, safety, easiness, affordable price, and accurate visualization of phased array in measuring the defect characteristics such as size, shape, depth, and orientation allow its wide usage and even as a substitute for other inspection methods like radiographic methods. In phased array methods, more than one sound wave is employed to enhance the reliability and flexibility of inspection through the operation of several transducer assemblies, involving 16 to 256 small individual elements. Titanium having many desirable properties like high strength to weight ratio found many industrial applications, which highly necessitates its persistent inspection. For this aim, the present study focuses on the application of the phased-array ultrasonic testing technique for inspecting Ti components specifically in additive manufacturing, welding inspections, and defect detection of complex geometries and composites. Moreover, modern applications and improved computation methods are discussed. The development of phased-array ultrasonic testing inspection systems, particularly high throughput and efficient in situ and mobile equipment, and new computation methods will open new horizons toward the highly sophisticated inspection procedures.