Surface Inspection Research Articles

High-performance surface defect detection of aluminum substrate based on event camera

Abstract Traditional industrial surface defect detection often employs CCD/CMOS cameras, but they are difficult to detect the minute defects on aluminum substrates in highly dynamic industrial scenes due to their nature. Event camera is a novel high-resolution vision sensor which measures per-pixel brightness changes in an asynchronous manner and outputs as event information flow (EIF). Small and weak defects on aluminum substrate can be captured by event camera effectively, but the EIF contain a large amount of noise, making it difficult to perform accurate and high-precision defect detection. To address this problem, we propose a frame aggregation method to realize good event information flow processing (EIFP), and then use an improved circle detection method to locate the aluminum substrate in each frame, removing abundant event information outside the aluminum substrate. Subsequently, we enhance the event signals under different frames based on optical flow tracking using multiple features, and construct a semi-supervised detector based on pseudo-labels to achieve high-precision defect localization. Finally, considering the small inter-class differences in defects on the surface of aluminum substrates, we construct a defect class corrector based on ensemble learning to enhance the ability to determine defect classes, achieving high-precision automatic quality inspection of aluminum substrate surfaces. The performance of our method is compared with other advanced methods based on event camera data of aluminum substrates in real industrial scenarios. The experimental results show that our method has improved the detection accuracy by about 10% and the classification accuracy by about 25% compared to the original state-of-the-art methods.

Fractographic investigation of carbon/epoxy PRSEUS composites exposed to flame after compressive failure

After a structural-related composite aircraft crash, a fractographic forensic analysis of the damaged surfaces is typically performed to assess the root causes of mechanical failures. Such accident reconstruction efforts, however, can be impeded if the aircraft catches on fire on the ground (i.e., a post-crash fire occurs), where flames or heat exposure can obscure or destroy the fracture surface morphologies of the fibers (i.e., the primary load carrying constituent). In this study, carbon/epoxy Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) skin-stringer assemblies were subjected to uniaxial compression and subsequently exposed to direct flame using a Bunsen burner. Specimens were oriented parallel, orthogonal, and at 45° to the flame axis for durations of 60 s. Additional vertical burn tests were performed for durations up to 300 s. Fractographic inspection of the failure surfaces before and after flame exposure was performed using a combination of destructive sectioning and scanning electron microscopy. The warp-knitted skin (fascia) surrounding the pultruded rod effectively served as a thermal protection layer, which shielded the rod’s broken filaments from significant thermal degradation and facilitated the identification of microbuckling and other mechanical failure mechanisms. This suggests that the presence of fascia, bulkheads, ribs, skins, and other intermediate layers in aircraft structures may significantly shield underlying principal structural element failure surfaces from fire exposure, facilitating post-crash forensic assessments of composite aircraft. Additionally, the through-thickness VectranTM stitching remained intact even after extended flame exposure, suggesting that such stitching can enhance the fire resistance of composite structures.

Extraction of isotropic and anisotropic components for optical surface micro-metrology based on the two-dimensional power spectral density analysis

Extraction of surface characteristics is essential during surface processing and optical inspections. In this work, we propose a new extraction method by dividing the global feature within multiple directions into isotropic and anisotropic components and combining noise filtration, two-dimensional component extraction, and fast reconstruction. The mathematical descriptions of global and local features were derived. The noise by holes, scratches, and finite sampling points was restrained by Bearing Ratio analysis, Hough transform, and Welch window operation. The isotropic and anisotropic surface components were extracted in the two-dimensional power spectral density domain, reconstructed in the two-dimensional Fourier domain, and inversed in Cartesian coordinates. Five surfaces with anisotropic structural characteristics ranging from zero to two dimensions were analyzed. The general applicability was proved according to the consistency between surface processing methods and extracted results. A chemical mechanical polishing experiment was designed and accomplished to verify the sensitivity of the extraction method. The subtle variation in surface morphology was captured on the reconstructed surfaces near the polishing end-point, confirming its detectability on weak anisotropic components. This process-oriented surface extraction method can achieve qualified results without transcendental knowledge of surface conditions and offers openness to various surface evaluation criteria by statistical roughness indicators, which supports surface inspection for multiple surface processing techniques.

Surface integrity analysis and inspection for nanochannel sidewalls using the self-affine fractal model-based statistical quality control for the atomic force microscopy (AFM)-based nanomachining process

The atomic force microscopy (AFM) technology is a promising method for nanofabrication due to the high tunability of this affordable platform. The quality inspection and control significantly impact the manufacturing effectiveness for realizing the functionality of the achieved nanochannel. Particularly, the surface characteristics of nanochannel sidewalls, which play a significant role in determining the quality of the nanomachined products, can not be accurately captured using conventional surface integrity metrics (e.g., surface roughness). Therefore, it is necessary to propose a method to quantitatively characterize the surface morphology and detect the abnormal parts/regions of the nanochannel sidewall. This paper presents a statistical process control approach derived from the self-affine fractal model to detect the sidewall surface anomalies. It evaluates changes in the self-affine fractal model parameters (standard deviation, correlation length, and roughness exponent), which can be used to signify the changes on the sidewall surface; the statistical distributions of these parameters are derived and used to develop control charts to allow inspection of the sidewall morphology. The approach was tested on the AFM-based nanomachined samples. The results suggest that the presented approach can effectively reflect the abnormal regions on the machined parts, which opens up a new avenue toward guiding the quality control and rework for process improvement for AFM-based nanomachining.

Geothermal Nano-SiO2 Waste as a Supplementary Cementitious Material for Concrete Exposed at High Critical Temperatures.

The partial replacement effect of Portland cement by geothermal nano-SiO2 waste (GNSW) for sustainable Portland-cement-based concrete was investigated to improve the properties of concrete exposed at high critical temperatures. Portland cement was partially replaced by 20 and 30 wt.% of GNSW. The partial replacement effect on Portland-cement-based concrete subjected to 350, 550, and 750 °C was evaluated by measuring the weight changes, ultrasonic pulse velocity, thermogravimetric and differential thermal analysis, X-ray diffraction, surface inspection, and scanning electron microscopy under residual conditions. The ultrasonic pulse velocity results showed that the GNSW specimens maintained suitable stability after being heated to 350 °C. The SEM analysis revealed a denser microstructure for the 20 wt.% of partial replacement of Portland cement by GNSW specimen compared to the reference concrete when exposed to temperatures up to 400 °C, maintaining stability in its microstructure. The weight losses were higher for the specimens with partial replacements of GNSW than the reference concrete at 550 °C, which can be attributed to the pozzolanic activity presented by the GNSW, which increases the amounts of CSH gel, leading to a much denser cementitious matrix, causing a higher weight loss compared to the reference concrete. GNSW is a viable supplementary cementitious material, enhancing thermal properties up to 400 °C due to its high pozzolanic activity and filler effect while offering environmental benefits by reducing industrial waste.

Mechanical properties and damage characterization of 310S steel using laser ultrasonic inspection under extreme temperature condition

Abstract To evaluate the mechanical properties of 310S stainless steel under extreme working conditions, and realize the damage characterization function, in this study, the longitudinal wave and Rayleigh wave of materials are measured by the method of coaxial inspection on reverse surface and ipsilateral ectopic inspection, respectively, and surface cracks and internal voids are detected by the scanning laser source technology and transmission scanning detection, respectively. The detection methods are designed based on the energy distribution characteristics of Rayleigh wave and longitudinal waves in laser ultrasound. The laser ultrasonic detection systems coupled with a temperature loading device (high temperature: vacuum chamber; low temperature: refrigerating chamber) developed by the laboratory enable on-site monitoring of laser ultrasonic technology in harsh environments. Experimental results demonstrate that the error in mechanical properties (elastic modulus, shear modulus, Poisson ratio) is less than 5% over a wide temperature range (-180-1000°C). At 1000°C, surface cracks wider than 0.5mm and deeper than 1.9mm as well as internal hole defects larger than 1.0mm can be detected with an error rate below 5%.

Surface profile inspection for large structures with laser scanning

Fringe projection profilometry is a powerful tool that is widely applied to shape measurement of objects in engineering. Limited by the light intensity of the projection unit, this technique is difficult to be applied to surface inspection of large structures, especially in outdoor applications. In this study, a line laser source is selected as the light projection unit. The line laser beam is controlled to scan the surface with the predefined angular speed while a stationary imaging unit captures images. An image fusion strategy has been proposed to construct grating images with a constant phase shift, which facilitates full-field phase shifting in determination of the structural profile. The accuracy of the measurement method is discussed and compared with a commercial 3D laser scanner. The proposed technique has also been applied to the surface topography of the wind turbine blades. The experimental results show that the bulging defects on the surface of the wind turbine blade model are detectable, which shows the feasibility of the proposed method in characterization of surface profile on large structures.

AdheScan – adhesive failure surface inspection system

AbstractAdhesive bonding is an established joining technique with many applications in for example aerospace, lightweight construction, and the automotive industry. It places particularly high demands on materials, processes, and quality assurance, requiring extensive development and qualification procedures. Many process samples generated for testing are still evaluated manually by experts. The AdheScan provides a quantifiable, simplified and more accurate evaluation of adhesive failure surfaces.

Stochastic geometry models for texture synthesis of machined metallic surfaces: sandblasting and milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

Design of hexapod robot equipped with omnidirectional vision sensor for defect inspection of pipeline’s inner surface

Abstract Defect detection of inner surface of precision pipes is a crucial aspect of ensuring production safety. Currently, pipeline defect detection primarily relies on recording video for manual recognition, with urgent need to improve automation, quantification and accuracy. This paper presents a hexapod in-pipe robot with carrying capacity designed to transport the omnidirectional vision sensor to specified location within unreachable pipelines. The feasibility of the robot’s mechanical design and sensor load-carrying module is analyzed using theory calculations, motion simulations and finite element method. To address the challenges of small pixel ratio and weak background changes in panoramic images, a tiny defect segmentor (TDS) based on ResNet is proposed for detecting tiny defects on the inner surface of pipelines. The hardware and software systems are implemented, and the motion performance of the pipeline robot is validated through experiments. The results demonstrate that the robot achieves stable movement at a speed of over 0.1m/s and can adapt to pipe diameter ranging from of 110 to 130 mm. The novelty of the robot lies in providing stable control of the loaded vision sensor, with control precision of the rotation angle and the displacement recorded at 1.84% and 0.87%, respectively. Furthermore, the proposed method achieves a detection accuracy of 95.67% for tiny defects with a diameter less than 3 mm and provides defect location information. This pipeline robot serves as an essential reference for development of in-pipe 3D vision inspection system.

Design and evaluation of a modular robot concept for external pipeline surface inspection

Design and evaluation of a modular robot concept for external pipeline surface inspection

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

Defect Detection of Topside Offshore Platform Structure Using Non-Destructive Testing Methods

Non-destructive testing on offshore rigs involves multiple sections. The topside platform structure, which consists of the main deck, cellar, and sub-cellar deck, is one of the most important sections. The purpose of this research is to use appropriate nondestructive testing (NDT) methods to assess the internal and surface defects as well as identify the material properties of the selected structure at the topside platform structure. Various NDT technologies can be utilized to detect faults on the topside of the offshore platform. The NDT techniques used in this study are ultrasonic, magnetic particle, and dye penetrant testing. To describe the material in the samples, a different method known as positive identification material is employed. Four tasks are involved in this process: identifying the material properties of each sample, identifying surface and interior faults, and comparing the two types of defects. The results of the analysis revealed that a 316L stainless steel specimen contained as much as 19.2% chromium, 11.8% nickel, and 3.0% molybdenum. Magnetic particle inspection was recommended for surface and subsurface inspection. In contrast, the ultrasonic testing examination yielded the best outcome for the topside offshore platform's interior problem.

An atomic force microscope-like dual-stage force controlled fast tool servo for in-process inspection of micro-structured surfaces

This study presents the development of a dual-stage force sensor integrated fast tool servo (FS-FTS) for in-process inspection of micro-structures. With the cutting tool serving as a probe, the measurement principle of the FS-FTS is similar to that of an atomic force microscope (AFM). The force modulation method, inspired by the tapping mode AFM, is employed to detect the weak contact between the cutting tool and the workpiece. The primary stage of the dual-stage FTS is responsible for tracking the surface profile of the micro-structures while the secondary stage offers the high-frequency oscillation to modulate the force signal. An experimental method is proposed to determine the optimal oscillation frequency. The transfer function of the FS-FTS, from the input voltage of the primary stage to the output force of the force detection module, is identified, and a notch filter integrated feedback controller is designed for the force control. With the designed dual-stage FS-FTS and force controller, the tip of the cutting tool could follow the unknown workpiece contour with a specified constant contact force, and the surface profile is obtained through the measured displacement of the primary stage. The feasibility and superiority of the developed system are validated through the in-process inspection of micro-lens with a depth of 19μm. The results demonstrate that the developed FS-FTS realizes the sub-micron profile measurement with a scanning speed of 150μm/min, and the depth of the resulting surface scratches is less than 20 nm.

Analysis of capacitive discharges in motor bearings under different operation conditions

AbstractDespite being a well‐known problem, bearing faults are still one of the major challenges that the industry face. The growing use of voltage source inverters imposed the alertness of possible bearing damage due to their high‐frequency signals. The aim of this paper is to investigate the effect of capacitive discharges on a rolling element bearing to obtain a series of faulty bearings with different deterioration levels. An ageing test bench is built to generate bearing roughness using Electric Discharge Machining. The purpose is to study the effect of operation conditions, such as frequency of drive, amplitude of shaft voltage, radial load level as well as rotational speed, on the capacitive discharge occurrence within the bearing. Having the desire to further understand this phenomenon, the influence of each parameter on the discharge activity and the discharge waveform is analysed. A worst‐case scenario is conducted and applied to a series of bearings. Finally, the bearing damage is examined, and a quantitative fault detection method is presented based on a microscopic inspection of the bearing's surface.

Effect of vacuum infiltration temperature on the microstructure and tribological properties of honeycomb-filled tube-reinforced aluminum matrix composites

The purpose of this work is to optimize the penetration temperature to obtain 316 L honeycomb-filled tube/aluminum matrix composite (HFT/AMC) with good wear resistance. Utilized selective laser melting technology to fabricate honeycomb-filled tubes as reinforcement for the composite material. HFT/AMC was prepared by filling aluminum into honeycomb-filled tubes under three different temperatures of 800 °C, 850 °C, and 900 °C through vacuum infiltration casting. And performed tissue characterization and performance testing. Microstructure, hardness, XRD, and EDS studies showed that the composite materials generate intermetallic compound transition layers of over 100 μm, and the overall strength of the interface was improved. A large number of intermetallic compounds and honeycomb-filled tubes distributed in the aluminum matrix inside the cells jointly strengthen the aluminum-based material. Inspection of worn surfaces of HFT/AMCs revealed abrasive and oxidative wear as the main wear mechanisms. Considering the wear of the produced HFT/AMC under certain conditions, the most suitable infiltration temperature is 850 °C (wear rate: 0.52 ± 0.16 [(mm3/N·m) × 10−3]). This study confirmed that honeycomb-filled tubes can significantly improve the tribological properties of aluminum-based materials.

P‐234: Late‐News Poster: A Data‐Centric Approach to Minimize Defect Leakage in an AI‐based Automated Surface Inspection System for Display Manufacturing Process

This study advances Surface Inspection (SI) in display panel manufacturing using a data‐centric approach, addressing high gray zone rates and data discrepancies. We employed a data flywheel method with dual labeling to improve dataset quality. Results show expanded automated coverage and enhanced classification, reducing defect leakage, demonstrating AI's impact in smart manufacturing processes.

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

ABSTRACT Eddy current array (ECA) technology is increasingly being used in the aerospace industry for non-destructive testing of aircraft components. This study evaluates the performance of ECA in detecting defects in aircraft components, focusing on its effectiveness, reliability, and sensitivity. The study evaluates the effectiveness of ECA technology in eddy current defectoscopy by introducing a dimensionless efficiency coefficient, then seeks to validate this coefficient through experimental testing of aircraft component materials with artificially induced defects of various sizes, types, and orientations to simulate real-world scenarios. ECA’s sensitivity in detecting small and subsurface defects is analyzed, along with precise defect sizing and positional information. Reliability and repeatability are investigated through repeated measurements. Furthermore, the article analyses the impact of various factors on the performance of ECA, including surface conditions, probe configurations, and inspection parameters. Comparative analysis is performed to assess the advantages and limitations of ECA in comparison to other conventional inspection methods. The findings of this study will contribute to a better understanding of the capabilities and limitations of ECA in detecting aircraft component defects. The results will aid in optimizing inspection strategies, enhancing the reliability of defect detection, and improving the overall maintenance practices in the aerospace industry.

Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging

Abstract In recent years, advanced deep learning techniques have emerged as pivotal tools in enabling the development of robust vision-based solutions for steel surface inspection. This resulted in enhanced inspection accuracy, all while significantly reducing costs in the manufacturing industry. However, the lack of actual steel surface defects datasets currently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, known for its prowess in image-related tasks, faces certain challenges, especially in classifying less common defects. This work proposes a novel hybrid CNN model with a Support Vector Machine (SVM) classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 and EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for classification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect dataset (X-SDD) are carried out and the accuracy and F1 scores are calculated for performance evaluation. The merged dataset contains eleven typical defect types with a total of 2660 defect images. Then, the adopted algorithm is compared with ten fine-tuned deep learning models to evaluate the performance of transfer learning for steel defect detection and identification. The evaluation results show that the deep feature extraction and SVM classification produced better results than the transfer learning. Finally, the proposed classifier model is validated on a newly collected dataset from a Computed Tomography scanner with an accuracy reaching over 96%.

Police custody: preventive or coercive police measure

Police custody: preventive or coercive police measure