Detection of internal defects using continuous wavelet transform and gradient streamlines in dual-channel speckle interferometry

This paper proposes a dual-function speckle interferometry system based on the temporal phase-shifting technique for both the digital speckle pattern interferometry (DSPI) and digital shearography (DS) measurement. The principle of the dual-function system has been described comprehensively based on the scalar diffraction theory. The simulation results show that the dual-function system which is based on the object beam multiplexing can simultaneously measure the out-of-plane displacement and slope under the single loading. The sensitivity of the out-of-plane displacement solved by the DSPI and DS method is discussed. Due to the error of the integration, the accuracy of the out-of-plane displacement calculation in DSPI is better than in DS. Finally, the dual-function speckle interferometry system has been proven effective in the edge detection of internal defects.

Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

Study on the 3D-printed surface defect detection based on multi-row cyclic scanning method

An experimental study has been made of the detection of structural defects in n‐type crystals by electrochemical etching under illumination. Under illumination, presence of structural defects such as mechanical damage or grown‐in defects on the crystal surface led to a decrease of dissolution rate of the surface. This enabled structural defects to be revealed as etch hillocks. Surface damage introduced during handling processes of the crystal was detected with high sensitivity. By observing etch hillocks, detailed information was obtained regarding fine structure and distribution of dislocations or dislocation clusters in nominally undoped crystals. It was found that novel defects, which were not involved in nominally undoped crystals , were involved in highly sulfur‐doped crystals with low dislocation densities .

Endoanal ultrasound is widely used for the detection of external and internal anal sphincter defects in patients with fecal incontinence. Recently, 3-dimensional transperineal ultrasound has been introduced as a noninvasive imaging method for the detection of these sphincter defects. This study was designed to assess agreement between 3-dimensional transperineal ultrasound and 2-dimensional endoanal ultrasound regarding the detection of anal sphincter defects in women with fecal incontinence. This study was designed as a prospective observational study. The study took place in a university hospital. Between October 2008 and June 2009, all women with concerns of fecal incontinence underwent 2-dimensional endoanal ultrasound as well as 3-dimensional transperineal ultrasound. The main outcome measures are the presence of external and internal anal sphincter defects. Fifty-five patients were included. External and internal anal sphincter defects were observed with 2-dimensional endoanal ultrasound in 27 (49%) and 15 (27%) patients. Three-dimensional transperineal ultrasound detected an external and internal sphincter defect in 19 (35%) and 16 (29%) patients. The Cohen κ coefficient for the detection of external (κ = 0.63) and internal (κ = 0.78) anal sphincter defects was good. This study's limitations include the absence of a surgical examination as the reference standard in the determination of sphincter defects. This study shows good agreement between 3-dimensional transperineal ultrasound and 2-dimensional endoanal ultrasound regarding the detection of anal sphincter defects. Based on these data, 3-dimensional transperineal ultrasound might be considered as a valuable alternative noninvasive investigation method.

In addition to causing damage to vehicles, road defects are one of the main causes of vehicle accidents which lead to loss of human lives. Many methods of detecting defects have been introduced over the years to reduce the consequences of these defects. One of these methods is image processing. Use of image and video processing has many applications in medicine, science, agriculture, and defect detection in structures. It has been used for defect detection on roads because timely detection and analysis of defect is very important for road serviceability and safety of the people. Detection of a defect by image processing broadly follows some of the basic steps which include feature extraction, edge detection, morphological operators, and training of data. Different approaches are used for various kinds of defect detection and analysis which have replaced the manual inspection method of roads saving time and resources. This chapter discusses the basic steps involved in defect detection using image processing along with existing systems that use machine learning and artificial intelligence for the detection of defects from a distance. To write this chapter, papers on the topic of image and computer vision-based defect detection systems have been consulted.

Composite materials have attracted significant attention with regard to the manufacturing of structures that require weight reduction, such as automobiles and aircraft, because they are more resistant to corrosion and fatigue than conventional metal materials. However, such materials exhibit a reliability degradation problem, i.e., their mechanical and physical properties deteriorate due to the occurrence of delamination and voids. Ultrasonic inspection methods have been widely applied for nondestructive detection of such defects in structures; however, the application of these approaches has been impeded due to high anisotropy and acoustic attenuation. In addition, the existing ultrasonic inspection methods require considerable time and cost for the inspection of large materials or structures. These problems were addressed in this study by developing an automatic ultrasonic inspection system; this was achieved by adopting a squirter-type water injection device, which uses a multi-joint robot and the through-transmission ultrasonic method. In addition, a software program to correct axis misalignment was developed and verified to solve the deterioration in defect detectability and accuracy that was caused by axis misalignment, which may occur during the use of the developed system. This development was accomplished after measuring the coordinates of the deformed mechanical part using a three-dimensional laser measuring instrument.

Accurately identifying the location and type of internal defects in gas‐insulated switchgear (GIS) is a challenge. To address this challenge, this study proposes a novel method for the nondestructive detection of GIS internal defects. This method is based on x‐ray digital radiography (X‐DR) technology and an improved real‐time models for object detection (RTMdet) algorithm, namely GIS‐specific localised internal defect‐RTMdet. Firstly, the X‐DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast. Then, a convolution shuffle upsample module for upsampling is proposed, which enlarges the defect feature map by multi‐convolution and pixel shuffling, reduces the information loss, and enhances the interaction between the feature information. Finally, both the multi‐channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association. Experiments demonstrate that the proposed method achieves a mean average precision @0.5:0.95 of 94.9%, showcasing excellent overall performance and generalisation ability, and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.

Finite element simulation of laser-generated ultrasonic waves for quantitative detection of internal defects in welds

Internal quality is an important aspect in the quality control and assurance of pickled products. A rapid and nondestructive method for internal defect detection would be of value to the pickle industry. A hyperspectral transmittance imaging technique was developed to detect internal defect in the form of carpel suture separation or hollow cucumbers resulting from dropping and rolling under load. Hyperspectral transmittance line scan images were collected from 'Journey' and 'Vlaspik' cucumbers over the six-day period after they were subjected to mechanical stress. Partial least squares discriminant analysis (PLS-DA) was performed on mean and standard deviation spectra extracted from the hyperspectral transmittance images to classify cucumber samples into defective or normal classes. A spectral-based pixel classification method using Euclidean distance was applied to classify pixels along the spatial dimension of the image into normal or defective class. The transmittance spectra of defective cucumbers were similar in shape to, but higher in magnitude than, those of normal cucumbers. Transmittance values for both defective and normal cucumbers were higher in the near-infrared range of 700-1000 nm than those in the visible range (450-700 nm). Average classification accuracies of 90.2%, 98.7%, and 95.4% were achieved using PLS-DA, whereas accuracies of 89.1%, 94.6%, and 90.5% were achieved using the spectral-based pixel classification method for Journey, Vlaspik, and the pooled data, respectively. The hyperspectral transmittance imaging technique can be used for rapid detection of internal defect in pickling cucumbers.

Measurement of dynamic crack tip displacement field by speckle photography and interferometry

We explore using a coherence domain reflectometry (OCDR) we developed to probe the inside of PIC chips. With clean and sharp reflection peaks having a spatial resolution down to 3 μm in Si, OCDR has the potential to detect and locate defects or anomalies inside PIC chips.

Active structural health monitoring methods are becoming increasingly popular for defect detection in hollow cylindrical structures utilized in aerospace applications. Electro mechanical impedance (EMI) based methods are commonly used to study the health of hollow cylinders in broad frequency ranges. However, the EMI analyzers are bulky and costly. In addition, EMI based methods require high quantity of sensors for monitoring a large area. The Surface Response to Excitation (SuRE) method was developed to eliminate the need for EMI analyzers in broad frequency range inspections and to decrease the number of sensors in monitoring of large structures. In this study, the performance of the SuRE method in detection of internal defects is evaluated theoretically and experimentally. Different internal damages were created in an aluminum pipe specimen. The data collected from four bonded piezoelectric elements were used for damage detection and quantification. The test results showed the high sensitivity and robustness of the SuRE method in internal damage detection.

Additive manufacturing (AM), also known as 3D printing, has revolutionized various industries by enabling the creation of complex and customized functional components. However, ensuring the quality and integrity of these components remains a significant challenge. Traditional quality inspection methods often fall short due to the intricate geometries and internal structures characteristic of AM parts. This paper explores the primary challenges in quality inspection for AM, focusing on the detection of internal defects. To address these challenges, we propose the integration of Artificial Intelligence (AI) with industrial Computed Tomography (CT) scanning as a comprehensive solution. Industrial CT scanning provides high-resolution, non-destructive imaging capable of revealing internal features and defects that are otherwise inaccessible. When coupled with advanced deep learning algorithms, these scans can be analyzed more economically, efficiently, and accurately than with conventional methods. AI enhances and speed up the inspection process by automating defect detection, facilitating the analysis of large datasets to identify patterns and anomalies with great repeatability. The integration of AI with CT scanning offers several advantages: improved accuracy and reliability of defect detection, reduced inspection times, and enhanced ability to segment and classify different patterns. This approach not only ensures higher quality standards for AM parts but also supports the scalability of additive manufacturing in critical industries such as aerospace, medical devices, and automotive. The paper concludes with experimental results demonstrating the effectiveness of AI-driven CT scan analysis in real-world applications, comparing with traditional methods, and exploring its potential to transform quality inspection in additive manufacturing.

Laser ultrasonics could be an attractive solution for the nondestructive testing of structures in harsh environments. Longitudinal waves generated in the ablation regime are especially well suited to internal defect detection because they provide a higher signal-to-noise ratio in comparison to ultrasonic waves generated under a thermo-elastic regime, while their propagation direction normal to the surface enables the simplified interpretation processing of received signals when the defect echoes are analyzed. The internal defect detection using laser-generated longitudinal waves in the ablation regime was investigated numerically and experimentally, and a numerical model to simulate the generation and propagation of ultrasonic waves in the ablation regime was developed. This model was based on the simulation of ultrasonic generation and propagation caused by the net reaction force that was directly converted from the laser intensity absorbed onto the surface. This model was also extended as a model for internal defect detection. A steel specimen containing artificial internal defects was fabricated and inspected by using the through-transmission (T-scan) and pulse-echo (B-scan) modes. Clear amplitude reduction was observed in the transmitted waves at the defect positions in T-scan images, while B-scan images clearly showed the defect echoes arriving at different times depending on the depth location of internal defects. These results demonstrate that longitudinal waves excited in the ablation regime can be effectively used for internal defect detection.