Defect Evaluation System Research Articles

GPR Imagery-Based Internal Defect Evaluation System for Railroad Tunnel Lining Using Real-Time Instance Segmentation

GPR Imagery-Based Internal Defect Evaluation System for Railroad Tunnel Lining Using Real-Time Instance Segmentation

Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud

ABSTRACT Wire and Arc Additive Manufacturing (WAAM) with high efficiency and low-cost is an economical choice for the rapid fabrication of medium-to-large-sized metallic components and has attracted great attention from scholars and entrepreneurs in recent years. However, defects such as porosity, and humps, could occur occasionally after each layer of deposition on weld bead surfaces due to disturbances and process abnormities. Detection and quantitative evaluation of weld bead defects is crucial to ensure successful deposition and the quality of the entire component. In this paper, a novel defect detection and evaluation system was developed for WAAM utilizing machine vision technology. The system incorporated new defect detection algorithms based on analysing the 2D curvature of the weld bead height curve and the 3D curvature of the weld bead point cloud. Furthermore, a defect evaluation algorithm was developed based on reconstructing the normal weld bead contour using geometric features extracted from the accumulated point cloud. This system enables the automatic detection of weld bead morphology during the WAAM process, offering important information about the location, type, and volume of defects for effective interlayer repairs and enhanced part quality.

Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

Compared to traditional approaches, the spot scanning surface defect evaluation system (SS-SDES) has better performances on the detection of small defects and defect classification for optical surfaces. However, the existing system deviations will cause distortions and even a missing area in the defect image which is reconstructed from the acquired raw data based on the scanning trace, thus degrading the reliability of detection results. To solve these problems, a system calibration method is proposed with the parameterization of these deviations and the modeling of practical scanning trace. A constraint function, to characterize the straightness and scale errors in the image, is defined. Then an optimization is implemented to minimize it and hence to obtain the optimal estimate of the system deviations, which is subsequently used to adjust the system and reconstruct reliable defect images. Additionally, to further enhance the image quality, an image reconstruction method capable of suppressing signal noise through a weighted average strategy is proposed. Experiments show that with our methods, the system deviations are effectively corrected, and a complete and precise defect image with low distortions that are within 1.8 pixels is reconstructed. Therefore, the detection accuracy and reliability of the system can be improved.

Surface defects evaluation system based on electromagnetic model simulation and inverse-recognition calibration method

Digitized evaluation of micro sparse defects on large fine optical surfaces is one of the challenges in the field of optical manufacturing and inspection. The surface defects evaluation system (SDES) for large fine optical surfaces is developed based on our previously reported work. In this paper, the electromagnetic simulation model based on Finite-Difference Time-Domain (FDTD) for vector diffraction theory is firstly established to study the law of microscopic scattering dark-field imaging. Given the aberration in actual optical systems, point spread function (PSF) approximated by a Gaussian function is introduced in the extrapolation from the near field to the far field and the scatter intensity distribution in the image plane is deduced. Analysis shows that both diffraction-broadening imaging and geometrical imaging should be considered in precise size evaluation of defects. Thus, a novel inverse-recognition calibration method is put forward to avoid confusion caused by diffraction-broadening effect. The evaluation method is applied to quantitative evaluation of defects information. The evaluation results of samples of many materials by SDES are compared with those by OLYMPUS microscope to verify the micron-scale resolution and precision. The established system has been applied to inspect defects on large fine optical surfaces and can achieve defects inspection of surfaces as large as 850mm×500mm with the resolution of 0.5µm.

Defects evaluation system for spherical optical surfaces based on microscopic scattering dark-field imaging method.

In the field of automatic optical inspection, it is imperative to measure the defects on spherical optical surfaces. So a novel spherical surface defect evaluation system is established in this paper to evaluate defects on optical spheres. In order to ensure the microscopic scattering dark-field imaging of optical spheres with different surface shape and radius of curvature, illumination with variable aperture angle is employed. In addition, the scanning path of subapertures along the parallels and meridians is planned to detect the large optical spheres. Since analysis shows that the spherical defect information could be lost in the optical imaging, the three-dimensional correction based on a pin-hole model is proposed to recover the actual spherical defects from the captured two-dimensional images. Given the difficulty of subaperture stitching and defect feature extraction in three-dimensional (3D) space after the correction, the 3D subapertures are transformed into a plane to be spliced through geometric projection. Then, methods of the surface integral and calibration are applied to quantitatively evaluate the spherical defects. Furthermore, the 3D panorama of defect distribution on the spherical optical components can be displayed through the inverse projective reconstruction. Finally, the evaluation results are compared with the OLYMPUS microscope, testifying to the micrometer resolution, and the detection error is less than 5%.

Alignment methods for micron-scale surface defects automatic evaluation of large-aperture fine optics

A surface defect evaluation system can combine microscopic scattering dark-field imaging with sub-aperture scanning and stitching. Thousands of sub-apertures are involved; mechanical errors will cause stitching dislocation, leading to defect cracks. In this Letter, we propose standard line coordinate error adjustment dealing with consistency error between coordinates of the scanning and imaging system, and defocus depth estimation leveling method dealing with high-cleanliness fine optics defocuing caused by the surface which is not perpendicular to microscope’s optical axis. Experiments show defect cracks are effectively solved and the defocus of 420 mm×420 mm components can be controlled within depth of field 20 μm.

Automated discrimination between digs and dust particles on optical surfaces with dark-field scattering microscopy.

To make the surface defects evaluation system (SDES) of fine flat optics more effective and reliable, the point-like defects on the surface are divided into two categories: digs and dust particles. Since only the digs are the real damages that should be sent for further investigation, the false signals associated with dust particles should be distinguished and removed. Dark-field scattering microscopy and pattern recognition methodology are combined to classify digs and dust particles. The SDES is employed for dark-field image acquisition of optical samples. Gray scale, texture, and morphology analyses are then conducted on each image to extract raw feature data, which are compressed with the principal component analysis. Based on the compressed feature data, the support vector machine is used to construct the classification model. The success discrimination rates are 96.56% for the training set and 93.90% for the prediction set. The classification results are presented to show the potential of this method to be used for practical digs and dust particles discrimination on the actual optical samples.

Applications of a laser assisted defect detection system for chemical mechanical planarization (CMP) slurry development in rigid disk polishing

A laser assisted optical surface analyser (OSA) was applied for inspecting scratch defects on rigid disks used for hard disk drives (HDDs). The disk samples were polished with a chemical mechanical planarization (CMP) technique beforehand. Since it has been recognized that the CMP slurry formulation highly affects the scratch performance of rigid disks, a reliable defect evaluation system is required for efficient slurry development. In this paper, the capability of the OSA tool (Candela C10, KLA-Tencor Corp.) for scratch evaluation is discussed with reference to the scratch data acquired with a conventional dark field microscope (DFM) technique. A gauge repeatability and reproducibility (R&R) study revealed that the Candela had more than three times better R&R performance than the DFM. Scratch counts measured using the two tools were correlated, but not matched, due to the differences in the detection systems. The linear regression correlation coefficient (R2) was found to be 71% for the relatively larger size (>1 mm) of scratch categories. Another study was conducted using a scratch control additive in CMP slurry. The results showed that the effect of the additive was readily detected by the Candela, but was not obvious for the DFM detection. Based on the findings, the similarities and differences of the Candela and the DFM techniques are discussed. It was also found that the tuning capability of the Candela analysis recipe allowed consistent scratch counts with another type of laser assisted defect evaluation tool. This flexibility of the Candela system is a main characteristic which can be effectively used for CMP slurry development. An approach to targeted slurry designs is suggested in conjunction with the advantages and opportunities of the Candela method.

Internal Quality Estimation of Watermelon by Multiple Acoustic Signal Sensing

Watermelons are usually sorted by theirs weight and internal quality. Some automated watermelon weight sorters have been developed and operated in watermelon production areas. However, inspection of internal quality of watermelon is still performed by manually. Principal method of identifying internal defect of watermelon is analyzing the percussion sound of watermelon by human experts. Development of non-destructive evaluation technique for internal quality of watermelon is required to reduce human decision errors. The objective of this study was to develop a non-destructive sorting system which can detect internal defect of watermelons. The internal defect evaluation system has a constant-force hitting hammer to generate the acoustic sound, a multi-point sound signal acquiring system, a noise removal circuit, and a signal processing and quality evaluation program. An internal quality prediction model by PLSR (Partial Least Square Regression) was developed by analyzing the percussion sound of watermelons. Using the developed model, the prediction result shows that the overall prediction accuracy was 90.1%, and severely defected watermelons were identified perfectly.

Laser spectroscopic study of defect equilibria in SrCl2

The site distribution of the different charge compensated sites in singly doped samples of SrCl2:Eu3+ and doubly doped samples containing a second rare earth ion has been measured by site selective laser spectroscopy for a range of different dopant concentrations and annealing temperatures. The site distribution exhibits the same anomalous behavior as was observed in alkaline earth halide crystals as long as the dopant levels are not too low. At very low dopant levels, the anomalous behavior is not observed and the dependence follows that expected for simple mass action descriptions of defect equilibria. An association constant is obtained at the low concentrations and it is found that the amount of association is much less than would be expected from theoretical estimates based on Harwell automatic defect evaluation system (HADES) models. Extensive clustering is observed at lower concentrations than in the alkaline earth fluorides. The number of different clusters and the types of clusters do not appear to have a strong dependence on the ionic radius of the dopant ions as would be expected from HADES calculations. The anomalous site distributions at the higher concentrations are attributed to additional interactions between defect structures that result from strain interactions. Since these interactions lead to concentration dependence of the free energy of formation for the different defect sites, it will be necessary to include these effects in models that are used to predict the relative dominance of different sites.

Computerised defect sizing using ALOK analysis in shrunk-on wheel of turbine rotor

A computerised defect evaluation system using an advanced ultrasonic technique for shrunk-on wheels of a turbine rotor has been developed. The flaw image was reconstructed by a modified ALOK method with attention to the tip diffraction echoes of stress corrosion cracking (SCC). The results of the application of this system to the mock-up wheel with artificial slits and real SCC cracks show that this system is very useful for crack sizing and locating.