Surface Inspection Research Articles

Corrosion Performance of Sn-Doped AlCrFeNiMn High-Entropy Alloy Synthesized via Arc-Melting Technique

Failure of materials such as steels during engineering applications can result in economic harm; hence, developing new corrosion-resistant materials is critical. In this work, high-grade powders of Al, Cr, Fe, Mn, and Ni were used to synthesize an equimolar AlCrFeNiMn high-entropy alloy (HEA) for potential chemical industry application. The cast alloy's properties were further altered by the addition of 1at%, 3at%, and 5at% tin (Sn) as alloying additives. To assess the impact of Sn on the alloy's resistance to corrosion, potentiodynamic polarization tests were conducted in various acidic and basic environments. Several surface inspection techniques, including scanning electron microscopy (SEM), optical microscopy (OPM), X-ray diffractrometry (XRD), and energy-dispersive X-ray spectroscopy (EDS), were employed to examine the morphological changes and elemental composition of the alloy after it was subjected to corrosive conditions. The nano-indentation machine was used to analyze the materials' nanohardness. TGA analysis was also performed to determine how Sn additions affected the AlCrFeNiMn HEA’s thermal stability. In 0.5 M HCl solution, the Sn-doped alloys demonstrated good corrosion resistance. Their exposure to 0.5 mol/L H2SO4 solution, on the other hand, found to be deleterious to their electrochemical stability. The weight loss of 5 at% Sn-doped samples in 0.5 M H2SO4 solution was found to be substantially reduced. The mass of all the samples stayed constant in 3.5 wt% NaOH solution.

Subsurface fracturing of sedimentary stones caused by bullet impacts.

The immovable nature of built heritage means that it is particularly vulnerable during times of armed conflict. Although impacts from small arms and shrapnel leave relatively inconspicuous impact scars, they elevate the risk of future stone deterioration. This study investigates the subsurface damage caused by bullet impacts, which is not apparent from surface inspection, in order to better understand the geometry and mechanics of this form of conflict damage to heritage. Controlled firearm experiments were conducted to simulate conflict damage to sandstone and limestone buildings. The bullet impacts created conical fractures or zones of increased fracture intensity below the impact, radial fractures, and spallation, in addition to a crater. Dynamic fracture distinguishes the formation of these features from quasi static cone crack experiments, while the lack of a shockwave differentiates these bullet impacts from hypervelocity experiments. Damage was created by momentum transfer from the bullet, so that differences in target properties had large effects on the nature of the damage. The crater in the limestone target was almost an order of magnitude deeper than the sandstone crater, and large open fractures formed in the limestone below the crater floor, compared with zones of increased fracture intensity in the sandstone target. Microstructural analysis of subsurface damage showed that fracture intensity decreased with increasing distance from the impact centre, suggesting that regions proximal to the impact are at increased risk of future deterioration. Conical subsurface fractures dipping away from the impact beneath multiple impact craters could link up, creating a continuous fracture network. By providing pathways for moisture and other weathering agents, fractures enlarge the region at increased risk of deterioration. Their lack of surface expression makes understanding their formation a vital part of future surveying and post conflict assessments.

Machine learning for rapid inference of critical dimensions in optical metrology of nanopatterned surfaces

Scatterometry-based metrology has the capability to perform high-throughput inspection of large-area nanopatterned surfaces. It utilizes physics-based dependencies between reflectance spectra of light scattered from nanopatterned surfaces and the geometric parameters, or so-called Critical Dimensions (CDs), of such nanopatterns. This paper proposes a novel method for rapid and accurate inference of CDs of complex nanopatterned surfaces through the use of a Machine Learning (ML)-based inverse problem mapping, coupled with the use of a strategic feature selection algorithm, which also originated from the ML domain. Specifically, Latin Hypercube Sampling was utilized to create a multi-dimensional grid of CDs which are used as inputs for physics-based simulations of scattered light spectra for nanopatterned surfaces with those CDs. Simulation results are used to build the training data for the ML model relating simulated reflectance spectra with the corresponding CDs. The Extreme Gradient Boosting (XGBoost) algorithm was utilized to realize the aforementioned mapping. In addition, most informative spectral wavelengths were selected using Recursive Feature Elimination (RFE) algorithm based on the feature importance scores obtained from XGBoost. Capabilities of the newly proposed approach were evaluated through inspection of a semiconductor wafer sample with hourglass patterns, which are characterized by eight CDs. It was observed that the proposed method is capable of real-time CD metrology of large-area nanostructured surfaces with complex nanopatterns, with accuracy and repeatability comparable to that of Scanning Electron Microscopy.

A Real-Time Inspection System for Industrial Helical Gears.

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects.

A Self-Supervised Model Based on CutPaste-Mix for Ductile Cast Iron Pipe Surface Defect Classification.

Online surface inspection systems have gradually found applications in industrial settings. However, the manual effort required to sift through a vast amount of data to identify defect images remains costly. This study delves into a self-supervised binary classification algorithm for addressing the task of defect image classification within ductile cast iron pipe (DCIP) images. Leveraging the CutPaste-Mix data augmentation strategy, we combine defect-free data with enhanced data to input into a deep convolutional neural network. Through Gaussian Density Estimation, we compute anomaly scores to achieve the classification of abnormal regions. Our approach has been implemented in real-world scenarios, involving equipment installation, data collection, and experimentation. The results demonstrate the robust performance of our method, in both the DCIP image dataset and practical field application, achieving an impressive 99.5 AUC (Area Under Curve). This presents a cost-effective means of providing data support for subsequent DCIP surface inspection model training.

BIM-supported drone path planning for building exterior surface inspection

Digitalization in the architectural, engineering, and construction (AEC) industry highlights the interdisciplinary collaboration between complex systems to provide fast and efficient services. This paper incorporates Building Information Modeling (BIM) and drone, and generates feasible paths for exterior building surface inspections. A systematic approach was proposed, focusing on the overall automatic procedure from the BIM model to the actual flight of the drone in a real-world building environment. The proposed method comprises five main steps: BIM to point cloud represented surface model generation, viewpoint determination, path planning, virtual simulation, and actual flight. In particular, the basic function could generate an inspection path for the whole building that guarantees high coverage rates, obstacle avoidance, and collision-free operation. The advanced function provides four types of building-specific decomposition strategies capable of overcoming the complex building structure shape, enabling reasonable inspection ranges, and enhancing computation efficiency. To simulate a drone’s inspection procedure and evaluate its performance in various contexts, an integrated platform based on a game engine and Airsim was developed. In addition, a case study was conducted to demonstrate the effectiveness of the method. The results demonstrate that the proposed BIM-enabled path planning can assist drones in conducting building inspection tasks with considerable applicability and flexibility.

Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems

The textile and apparel industry is one of the largest industries in the world, with significant environmental and social impacts, such as manual labor in poor working conditions and resource depletion and environmental degradation due to ineffective production. As a result, there is an increasing need for sustainable practices and a transition toward a more circular and responsible industry. In this paper, we introduce the proposed approach in the context of a sustainable transition for the industrial textiles. Instead of human labor, the automated solution to detect defects is proposed for the 4.0 revolution in general and the textile industry in particular. Specifically, the mechanical design of a three-dimensional model for small- and medium-sized fabric rolls is portrayed. Some computational mechanics are created to ensure a stable mechanism and proper actuators. For acquiring accurate data and measuring the machine state, several installations, that is, a digital camera, load cell, and positioning sensor, and the layout of the light source are improved to intensify the image quality. The control theme, such as the cross-coupling controller, offers superior performance in synchronization and vibration-less motion. Lastly, the excellent detection method is provided by the convolutional neural network scheme for offline training to distinguish defects on products. Instead of a manual check or low rate of defect detection on products, the contributions of this research are as follows: (a) analyzing the mechanical design of the textile machining system in terms of dynamic characteristics; (b) implementing a smooth scheme based on motion control synchronization; and (c) integrating various strategies, including vision-based computation and control topology, to demonstrate superior performance. These results highlight that our approach can be widely adapted to address sustainability challenges not only in the textile field but also in global manufacturing practices.

Online surface defect segmentation on aluminum strip production line using a lightweight and efficient model

A significant challenge in automated defect inspection (ADI) of aluminum strip surfaces is improving segmentation speed to satisfy the online inspection requirements of the production line while maintaining the precision of defect identification. This study proposes a lightweight and efficient defect segmentation model that can be applied in aluminum processing enterprises for fast and precise segmentation of aluminum strip surface defects. A novel fusion attention (FA) mechanism is first established to enhance the focus on critical characteristics along the spatial and channel dimensions. This mechanism adopts continuous dilated convolutions with appropriate dilation rates to effectively increase the range of the receptive field and improve defect localization accuracy. Subsequently, a lightweight MobileViTv2 with an embedded FA mechanism is employed as a multi-scale feature extractor to learn comprehensive representations from defect images. Next, a novel feature fusion method, named large-scale feature pyramid network (LSFPN), is introduced to enhance the focus on details within large-scale features. LSFPN establishes four progressively shallower top-down pathways with fast normalized fusion weights and incorporates lightweight aggregation nodes based on the MoblieNetv2 block. Surface images of straightened aluminum strips with five universal defects were collected, whereby a new dataset was established. The experimental outcomes demonstrate the proposed model outperforms other state-of-the-art techniques synthetically, achieving a mean Intersection over Union (mIoU) of 87.01%, a segmentation speed of 61.67 fps, and a model size of 16.23 MB. This model may serve as a valuable theoretical foundation for the online segmentation of aluminum strip surface defects in embedded devices.

Durability Assessment of the Laureano Gómez Bridge: a Key Element for New Colombian Infrastructure

AbstractThe durability of reinforced concrete bridges is crucial for ensuring their long‐term functionality and safety. Among the various deterioration mechanisms, carbonation and chlorides are the most significant and widespread problems affecting these structures. This paper aims to assess the durability condition of the historical Colombian Bridge “Laureano Gómez”, which has been in service for over 40 years. The condition assessment involved measurements of the concrete cover, carbonation depths, concrete resistivity, compressive strength, crack survey, and visual inspection of the steel surface. Estimating the remaining service life showed that the stay elements are the most prone to steel depassivation due to carbonation, primarily due to low concrete cover and exposure conditions. The insights from this study could provide valuable information for the durability prognosis of the new bridge built next to it.

Steel Surface Defect Detection Based on SSAM-YOLO

The defect inspection of the steel surface is crucial to modern manufacturing and highly depends on inefficient manual work. The emergence of deep learning has prompted the development of automated defect detection methods, but the current methods perform badly in the detection of the crazing and rolled-in scale-two types of defects on steel surfaces. The difficulty in the detection of crazing and rolled-in scale is mainly due to the similarity between object regions and background regions. Based on this, the authors propose a supervised spatial-attention module (SSAM). It introduces a priori knowledge compared to the traditional spatial attention mechanism, which can enhance the supervision of relevant parameters in the attention mechanism module during network training. Finally, they introduced the SSAM to the YOLOv5 and got the SSAM-YOLO. The test result on the NEU-DET dataset shows that the proposed method has better detection accuracy, achieving improvements of 7.3% and 3.02% on the AP@0.5 for the crazing and rolled-in scale. The method also outperforms the comparative main stream algorithms for steel surface defect detection, verifying the effectiveness of our algorithm.

Surface Electric Potential Measurement with a Static Probe

Surface electric potential measurements are widely used in non-destructive inspection and testing of precision surfaces, for example, in the production of semiconductor devices and integrated circuits. Features of the construction and application of devices for measuring the surface electric potential using an immovable reference electrode are considered. Despite the need to increase the area of the probe compared to devices with a vibrating probe, measurement techniques with an immovable probe have a number of advantages and could expand the scope of surface electric potential measurements in the inspection of samples with precise surfaces. Models of the formation of a measuring signal in the presence of a spatial inhomogeneity of surface electric potential are presented and discussed.

Structural Design and Simulation of a Multi-Channel and Dual Working Condition Wafer Defect Inspection Prototype.

Detecting and classifying defects on unpatterned wafers is a key part of wafer front-end inspection. Defect inspection schemes vary depending on the type and location of the defects. In this paper, the structure of the prototype is designed to meet the requirements of wafer surface and edge defect inspection. This prototype has four inspection channels: scattering, reflection, phase, and contour, with two working conditions: surface and edge inspection. The key structure of the prototype was simulated using Ansys. The simulation results show that the maximum deformation of the optical detection subsystem is 19.5 μm and the fundamental frequency of the prototype is 96.9 Hz; thus, these results meet the requirements of optical performance stability and structural design. The experimental results show that the prototype meets the requirements of the inspection sensitivity better than 200 nm equivalent PSL spherical defects.

Vision-Based System for Black Rubber Roller Surface Inspection

This paper proposes a machine vision system for the surface inspection of black rubber rollers in manufacturing processes. The system aims to enhance the surface quality of the rollers by detecting and classifying defects. A lighting system is installed to highlight surface defects. Two algorithms are proposed for defect detection: a traditional-based method and a deep learning-based method. The former is fast but limited to surface defect detection, while the latter is slower but capable of detecting and classifying defects. The accuracy of the algorithms is verified through experiments, with the traditional-based method achieving near-perfect accuracy of approximately 98% for defect detection, and the deep learning-based method achieving an accuracy of approximately 95.2% for defect detection and 96% for defect classification. The proposed machine vision system can significantly improve the surface inspection of black rubber rollers, thereby ensuring high-quality production.

A hybrid descriptor for low-textural image stitching in real-time surface inspection systems

A hybrid descriptor for low-textural image stitching in real-time surface inspection systems

Ad-hoc solutions for ultrasonic inspection of highly complex aircraft composite structures

Nowadays, the ultrasonic inspection (UT) is the most common non destructive technique (NDT) for composite components in the aerospace industry, due to its high accuracy, reliability and the degree of industrialization. Although UT is widely developed, new manufacturing processes and part concepts are continuously pushing the technology for new improvements and applications. This is the case of the geometries resulting from novel out of autoclave composites manufacturing technologies, Liquid Resin Infusion (LRI) or Resin Transfer Moulding (RTM), allow the fabrication of parts in one shoot, with focus in bringing answers to the challenge of manufacturing fully assembled parts and avoiding secondary assembly phases but also limiting the accessibility for NDT inspections. In this sense, one of the critical issues in the aeronautic sector is the improvement of the inspection of geometries with difficult access and small dimensions. The requirement of the aeronautic industry of guaranteeing the quality of the primary composite structures implies the inspection of the whole component through certified technologies. This constraint, together with the lack of technological solutions for the inspection of regions with limited accessibility is blocking the industrial implementation of optimized manufacturing processes. The present work describes the design and development of adaptive hardware solutions for the single and phased array UT inspection of internal structures on a highly integrated composite aeronautic component. The final solution has been obtained as result of an iterative design process and manufactured through additive manufacturing technology (Powder Bed Laser Fusion, formerly Selective Laser Sintering) using polyamide. This device was validated in a for inspection of flat surfaces of stiffener internal structure, belonging to the composite flap demonstrators of lengths: 3 meters in a first approach, and 8 m for final validation. This novel inspection solution has been performed in the framework of the FLAP project to develop and innovate the manufacturing process for complex mobile surfaces in a collaboration between Aernnova, through its Composites Division, and CATEC teams.

An investigation on performance of human visual and tactile perception in machined surface inspection

Comparative determination of surface roughness by visual and tactile comparison is employed in preliminary part evaluation and quality control in the machining industry. This on-stand evaluation technique allows for quick verification of the product’s adherence with surface roughness specifications. While regarded as less precise than measurement with contact profilometers, it is a fast method that can be employed after basic training. The work conducted by the authors of this study concerned experimental trials under conditions resembling real-life part examination scenarios in industrial settings. Human test subjects were tasked with determining the rougher sample in a pairing in two-alternative forced choice trials, with employed samples taking the form of flat end milled surfaces. Three distinct modes of inspection were involved, namely visual inspection and combined visual and tactile inspection by vertical and horizontal sliding touch, respectively. The obtained results show that the majority of study participants were capable of correctly determining the rougher sample in the pairing, regardless of sensory conditions. Variability in the ratio of correct responses was observed, depending both on stimulus intensity and the employed inspection mode. Overall best performance in terms of ratio of correct responses and variability in responses between participants was noted for combined inspection using vision and vertical sliding touch.

An adaptive weighting multimodal fusion classification system for steel plate surface defect

In steel surface inspection, an accurate steel surface defect identification method is needed to evaluate the impact of defects on structural performance and system maintenance. Traditionally, the recognition accuracy of methods based on handcrafted features is limited, but the system performance can be improved by feature fusion extracted by different methods. Therefore, this research uses the pre-trained convolutional neural network (CNN) combined with transfer learning to extract effective abstract features, and carries out adaptive weighting multimodal fusion of three the abstract features and handcrafted feature sets at the decision-making level, that is, proposes an adaptive weighting multimodal fusion classification system. The system uses handcrafted features as a supplement to abstract features, and accurately classifies steel surface defects in completely different feature representation spaces. Based on the NEU steel plate surface defect benchmark database, the classification results of feature sets before and after fusion are compared and analyzed. The experimental results show that the classification accuracy of the fusion system is improved by at least 3.4% compared with that before fusion, and the final accuracy rate is 99.0%, which proves the effectiveness of the proposed system.

Progressive refinement imaging with depth-assisted disparity correction

In recent years, the increasing on-board compute power of mobile camera devices gave rise to a class of digitization algorithms that dynamically fuse a stream of camera observations into a progressively updated scene representation. Previous algorithms either obtain general 3D surface representations, often exploiting range maps from a depth camera, such as, Kinect Fusion, etc.; or they reconstruct planar (or distant spherical, respectively) 2D images with respect to a single (perspective or orthographic) reference view, such as, panoramic stitching or aerial mapping. Our work sets out to combine aspects of both, reconstructing a 2.5-D representation (color and depth) as seen from a fixed viewpoint, at spatially variable resolution. Inspired by previous work on “progressive refinement imaging”, we propose a hierarchical representation that enables progressive refinement of both colors and depths by ingesting RGB-D images from a handheld depth camera that is carried through the scene. We evaluate our system by comparing it against state-of-the-art methods in 2D progressive refinement and 3D scene reconstruction, using high-detail indoor and outdoor data sets comprising medium to large disparities. As we will show, the restriction to 2.5-D from a fixed viewpoint affords added robustness (particularly against self-localization drift, as well as backprojection errors near silhouettes), increased geometric and photometric fidelity, as well as greatly improved storage efficiency, compared to more general 3D reconstructions. We envision that our representation will enable scene exploration with realistic parallax from within a constrained range of vantage points, including stereo pair generation, visual surface inspection, or scene presentation within a fixed VR viewing volume.

Physical Coupling Fusion Sensing of MFL-EMAT for Synchronous Surface and Internal Defects Inspection

Non-destructive testing (NDT) techniques are widely used for inspection and evaluation of conductive materials. However, a single physical mechanism sensing cannot simultaneously satisfy multiple inspection requirements. In this paper, we propose a hybrid transducer based on magnetic flux leakage (MFL) and electromagnetic acoustic transducer (EMAT) for synchronous detection of surface and internal defects. In the proposed hybrid transducer, both MFL and EMAT share a common magnetic field. The magnetic circuit characteristics of the MFL and EMAT principles are fully exploited, and the magnetic field provided by a single permanent magnet is used to excite ultrasonic waves that can detect internal defects within the sample. Iron is used to introduce magnetic field into the sample and provide a horizontal magnetic field to detect discontinuities on the near-surface of the sample. The huge frequency difference between the MFL signal and the EMAT signal effectively suppresses interference between the two signals. Simulations and experiments have been undertaken to show that the proposed transducer can overcome the detection limitations associated with the MFL and the dead detection zone of the EMAT, while simultaneously detecting the surface defects, internal defects and bottom thinning defects in the specimen.

Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization

Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization