Automated Defect Recognition Research Articles

Automated Defect Recognition Using Spline Interpolation

Existing infrastructure systems are aged and deteriorated rapidly, state agencies started searching for more advanced way to maintain their valuable assets to the acceptable level. One of them is the application of digital image processing. This problem proposes a digital image processing method to assess a steel bridge coating surface. The image processing method was developed based on B-Spline interpolation and can be used to recognizing the existence of bridge coating rust defects. An automated defect recognition method can make a decision whether a given digitized image contains defects by image comparison. Key Words:B-Spline interpolation, Digital Image Processing, Automated defect recognition method, Image comparision

Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications.

Automated defect recognition (ADR) for monitoring industrial components using neural networks with phased array ultrasonic images

In this paper, we propose a framework to automate the process of defect characterizing for industrial structural component health monitoring by implementing automatic defect recognition (ADR) system. The ADR system consists of a convolutional neural network (CNN) and an edge detection algorithm medial axis transform (MAT). The CNN learns the defect feature space from the training dataset to detect and classify the defect. The MAT algorithm is used upon post-validation of the ADR, and the predicted feature’s edges are extracted to size them. The ADR is trained using the simulation-assisted finite element (FE) simulation datasets consisting of side drilled holes (SDH) and crack defects images. The training datasets are generated by introducing virtual array source aperture (VASA), which is a full matrix capture (FMC) scanning strategy by activating the group of elements in an active aperture with predefined focal laws to form a focused beam at a virtual source in the material. The VASA technique uses multiple virtual sources and active aperture positions in a given transducer, which are determined using the Poisson point process. The ultrasound beam is excited in sequence on each virtual source, and the reflected wave is recoded using all the transducers in the array to create FMC A-scans signals. The total focusing method (TFM) technique is a postprocessing algorithm implemented on the FMC signal to generate an image. A large quantity of training datasets is created for each defect by modeling various FE models with varying defect morphology. To create nearly close to experimental images, the experimental noise is introduced in the simulated images. The three separate ADR systems are trained with individual defects class and combined defects. The effectiveness of the trained ADR system is validated by conducting experiments on the plates with laboratory-made SDH and crack defects, the casting components, and weldments with unknown defect types and sizes. The mAP of ADR training is 82%, and the F1-score on testing image classification is 89%. The ADR system could detect and size the smallest defect is 0.219 mm, which is λ L /5.

Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data

In this contribution, we investigate a methodology based on neural networks for efficient learning of light metal castings for defect detection in X-ray imaging. The motivation comes from the high effort in time and costs which is currently required to set up new objects or parts. To overcome this drawback, on the one hand, we want to reduce the complexity for the user by applying neural networks for defect detection. On the other hand, we try to use as much data from the simulation as possible instead of real data which is costly to collect. The performance of the investigated approaches is evaluated using real-world data from wheel inspection. We show that training on simulated data only is inferior to training with costly real world data. Combining both types results in the best performance and the closer the simulated data matches the real-world, the better the performance.

Automated Defect Recognition for Additive Manufactured Parts Using Machine Perception and Visual Saliency.

Metal additive manufacturing (AM) is known to produce internal defects that can impact performance. As the technology becomes more mainstream, there is a growing need to establish nondestructive inspection technologies that can assess and quantify build quality with high confidence. This article presents a complete, three-dimensional (3D) solution for automated defect recognition in AM parts using X-ray computed tomography (CT) scans. The algorithm uses a machine perception framework to automatically separate visually salient regions, that is, anomalous voxels, from the CT background. Compared with supervised approaches, the proposed concept relies solely on visual cues in 3D similar to those used by human operators in two-dimensional (2D) assuming no a priori information about defect appearance, size, and/or shape. To ingest any arbitrary part geometry, a binary mask is generated using statistical measures that separate lighter, material voxels from darker, background voxels. Therefore, no additional part or scan information, such as CAD files, STL models, or laser scan vector data, is needed. Visual saliency is established using multiscale, symmetric, and separable 3D convolution kernels. Separability of the convolution kernels is paramount when processing CT scans with potentially billions of voxels because it allows for parallel processing and thus faster execution of the convolution operation in single dimensions. Based on the CT scan resolution, kernel sizes may be adjusted to identify defects of different sizes. All adjacent anomalous voxels are subsequently merged to form defect clusters, which in turn reveals additional information regarding defect size, morphology, and orientation to the user, information that may be linked to mechanical properties, such as fatigue response. The algorithm was implemented in MATLAB™ using hardware acceleration, that is, graphics processing unit support, and tested on CT scans of AM components available at the Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) at Penn State's Applied Research Laboratory. Initial results show adequate processing times of just a few minutes and very low false-positive rates, especially when addressing highly salient and larger defects. All developed analytic tools can be simplified to accommodate 2D images.

Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning

In this work, process monitoring data, including layerwise imagery, multi-spectral emissions, and laser scan vector data, were collected during laser-based powder bed fusion additive manufacturing and correlated to fatigue performance. All parts were X-ray CT scanned post-build, and internal flaws were identified via an automated defect recognition software. Convolutional neural networks were trained to discriminate flaws from nominal build conditions using in situ data modalities only. Trained classifiers were then tested against a previously unseen data set collected from an independent build, and classification performance and metrics for information content provided by each individual modality were formally established. Correlations were drawn between the detected flaw populations and the corresponding fatigue properties, demonstrating that fatigue critical lack-of-fusion flaws can be detected via machine learning of in situ sensor data. The present results also show that, at least from a classification accuracy perspective, flaw detection via ML on process monitoring data is a viable path forward for real-time flaw detection and automated, interlayer repair strategies. However, strategies for extracting and analyzing sensor data in real-time without incurring excessive increases in build time must first be developed. These developments represent necessary components to draw direct correlations between in situ data modalities, internal part quality, and fatigue performance.

Defect Recognition of Roll-to-Roll Printed Conductors Using Dark Lock-in Thermography and Localized Segmentation

The demand for flexible large area optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic photovoltaics (OPVs) is growing. Roll-to-roll (R2R) printing enables cost-efficient industrial production of optoelectronic devices. The performance of electronic devices may significantly suffer from local electrical defects. The dark lock-in infrared thermography (DLIT) method is an effective non-destructive testing (NDT) tool to identify such defects as hot spots. In this study, a DLIT inspection system was applied to visualize the defects of R2R printed silver conductors on flexible plastic substrates. A two-stage automated defect recognition (ADR) methodology was proposed to detect and localize two types of typical electrical defects, which are caused by complete or partial breaks on the printed conductive wires, based on localized segmentation and thresholding methods.

Automated Defect Recognition of Castings Defects Using Neural Networks

Industrial X-ray analysis is common in aerospace, automotive or nuclear industries where structural integrity of some parts needs to be guaranteed. However, the interpretation of radiographic images is sometimes difficult and may lead to two experts disagree on defect classification. The automated defect recognition (ADR) system presented herein will reduce the analysis time and will also help reducing the subjective interpretation of the defects while increasing the reliability of the human inspector. Our convolutional neural network (CNN) model achieves 0.942 mAP@IoU = 0.50, which is considered as similar to expected human performance, when applied to an automotive aluminium castings dataset (GDXray). On an industrial environment, its inference time is less than 400 ms per 16 GB DICOM image (16 bits), so it can be installed on production facilities with no impact on delivery time. In addition, an ablation study of the main hyper-parameters to optimise model accuracy from the initial baseline result of 0.75 mAP up to 0.942 mAP, was also conducted.

Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach.

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

Automated Defect Recognition for Welds Using Simulation Assisted TFM Imaging with Artificial Intelligence

In this paper, Artificial Intelligence (AI) algorithms are employed for first, automating the process of creating a large synthetic Total Focusing Method (TFM) imaging dataset using a small set of Finite Element (FE) simulation datasets, and second for the automated defect-recognition (ADR) in butt-welds. In this paper, six types of imaging datasets are created with three approaches. In the first approach, weld TFM images are constructed using ultrasonic A-scan signals obtained from Full Matrix Capture (FMC) performed using FE analysis on models with weld defects (porosity and slag). The second approach generates near real-time weld TFM images by implementing fast deep convolution generative adversarial networks (DCGAN). This second technique permits simulations that are several orders faster when compared to the FE method. In the third approach, noise is extracted from FMC-TFM experimental measurements using the sliding kernel approach, and this noise is supplemented to individual simulated datasets for creating near to realistic scenarios. The first dataset is created using the first approach. The second dataset is created using the second approach, and the third hybrid dataset is a combination of FE and DCGAN weld TFM imaging. The fourth dataset is noise supplemented to FE based dataset. The fifth dataset is generated by adding noise to DCGAN images. The sixth hybrid dataset with noise is a combination of FE and DCGAN weld TFM noise images. AI plays a significant role in object detection and classification through robust feature extraction, reducing human intervention. In this work, for automated weld defect recognition, a convolutional neural network (CNN) is trained using six types of simulation-assisted weld TFM imaging datasets, which improves the reliability and efficiency of welds quality assurance. The mAP value is 85% for the ADR model trained using the hybrid weld TFM dataset with noise. The model prediction on classification on the hybrid dataset for porosity is 0.86 F1-score, and for slag is 0.80 F1-score.

Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

For defense applications, rapid X-ray inspection of propellant samples is essential for the identification and assessment of defects. Automation of this process using artificial intelligence is possible by properly training a neural network model. Convolution Neural Networks (CNNs) have recently demonstrated excellent success in both the tasks of image recognition and localisation using an adequate amount of data. In real-world, it’s not an easy task to produce the correct amount of experimental data required for the deep neural network to operate. In this work, we propose a method for producing synthetic radiographic data that is supported by ray tracing based radiographic simulations for the deep learning algorithms to automatically detect anomaly in X-ray images. The simulation results, which are then supplemented by noise extracted from the experimental data, show a good comparison with the measurements. This Simulation assisted Automatic Defect Recognition (Sim-ADR) system simultaneously perform defect detection and defect instance segmentation. The accuracy of the defect detection system is more than 87% on a testing set included 416 images.

Computer-Aided Recognition of Defects in Welded Joints during Visual Inspections Based on Geometric Attributes

An automated defect recognition algorithm is presented for detecting and classifying weld defects by photographic images. The proposed recognition algorithm selects a defective domain in a segmented image, extracts geometric features from the image, and relates the defect to one of six classes: no defect, cavity, longitudinal crack, transverse crack, burn-through, or multiple defect. The algorithm is implemented in the Matlab 2018b MathWorks environment and has been tested on 60 photographs of defects of various classes; the accuracy of recognition was 85%.

First attempts in automated defect recognition in superconducting radio-frequency cavities

The inner surface of superconducting cavities plays a crucial role to achieve highest accelerating fields. The industrial fabrication of cavities for the European X-Ray Free Electron Laser (EXFEL) and the International Linear Collider (ILC) HiGrade Research Project allowed for an investigation of this interplay with a large sample on different cavities undergoing a standardized procedure. For the serial inspection of the inner surface, the optical inspection robot OBACHT was constructed and to analyze the large amount of data, represented in the images of the inner surface, an image processing and analysis code was developed. New variables to describe the cavity surface were obtained. Two approaches using these variables and images to automatically detect defects has been implemented and tested. In addition, a decision-tree based approach of classifying defect free surfaces regarding their accelerating performance was tested and found to be physically valid.

Automated defect analysis in electron microscopic images

Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

Innovative approach toward an algorithm for automated defect recognition for on-load-tap changers

Power transformers are valuable assets in the power network, with the primary function to regulate the transmission and distribution voltage. The regulating device is the tap changer which sets the turn ratio. Tap changer failure is one of the leading causes of high-voltage power transformer failure. Most tap changer failures are caused by degrading contacts. Contact degradation may be the result of contact wear or of arcing-induced carbon deposition during on-load-tap (OLT) changes. Contact degradation increases contact resistance, causing increased heating and arcing, eventually leading to possible failure of the power transformer. Proper maintenance, therefore, requires a diagnostic system which can assess the condition of the contacts. The research presented is aiming at automated defect recognition and localisation from measured dynamic resistance measurement patterns, for two types of tap changers, the 'Diverter Switch' type and the 'Selector Switch' type OLT changer.

Automated Defect Recognition and Identification in Digital Radiography

Weld quality assurance is important for the safe exploitation of many products and constructions. This paper summarizes work on an advanced system for automated radiogram analysis. The most important parts of the process of radiogram analysis such as segmentation, thresholding and defect recognition and classification are discussed. A complex classifier composed of artificial neural networks and a fuzzy logic system is proposed and discussed in detail. The proposed classifier shows better performance and flexibility than the normal neural networks classifiers.

Development and performance testing of the world’s first automated individual body armor inspection system

With the onset of conflict and large troop deployments into South West Asia in 2003 during Operation Iraqi Freedom, the Radiography Laboratory at the US Army Armaments, Research, Development and Engineering Center at Picatinny Arsenal, NJ, USA, was approached to investigate the feasibility of designing an automated inspection system to examine the integrity of all the small arms ceramic armor plates currently in service. This amounted to ~1 million units; therefore, the need for a high-throughput, fully automated system was apparent, and this included automated defect recognition software and automated material handling.

Two-Stage Automated Defect Recognition Algorithm for the Analysis of Infrared Images

In this article an algorithm for the analysis of raw thermal infrared images is proposed. The images are obtained by using the nondestructive evaluation method of the laser-spot thermography and aim at detecting the presence of surface defects. A laser is used to scan a test specimen through the generation of single pulses. The temperature distribution produced by this thermoelastic source is measured by an infrared camera and processed with a two-stage algorithm. In the first stage, simple mathematical and statistical parameters are used to flag the presence of damage. Then, once damage is detected, the thermal image's first and second spatial derivative and two spatial filters are computed to enhance contrast, and to locate and size the defect. Some of the advantages of the proposed method with respect to existing approaches include automation in the defect detection process and better defective area isolation through increased contrast. The algorithm is first proven by analyzing simulated thermal images, and then it is experimentally validated by scanning the surface of a CFRP composite plate with induced defects.

Design, development, testing and deployment of theworld's first fully automated individual body armor x-ray inspection system

With the onset of conflict and great troop deployments into South West Asia in 2003 during Operation Iraqi Freedom, the Radiography Laboratory at the US Army ARDEC was approached to investigate the feasibility of designing an automated inspection system to examine the integrity of all the Small Arms Protective Insert (SAPI) ceramic armor plates currently in service. This amounted to on the order of 1 million units so the need for a high-throughput, fully automatedsystem was apparent; this included automated defect recognition (ADR) software and automated material handling (AMH). Among the most challenging imaging artifacts is the fine local variation that arises as a combination of imager noise, photon noise and as a result of the arrangement of the fibers themselves in the plate backing and cover material. In many of these cases, the crack indications are actually smallerthan the level of interference imposed by the fine local variations.