Vision-based Inspection System Research Articles

Vision-based inspection system for cladding height measurement in Direct Energy Deposition (DED)

A vision-based inspection system based on three digital cameras is proposed for measuring the cladding height in the Direct Energy Deposition (DED) process. To improve the accuracy of the cladding height measurements, an image processing technique is applied to remove the undesirable zone from the binary image. Furthermore, since the unit length in the captured images is different to that in the world coordinate framework, a calibration bar method is designed to transform the pixel value to the real size. In the proposed approach, a calibration bar method is employed to compensate for the Field-of-View (FOV) and perspective effects in the trinocular system. An image-processing technique is then employed to isolate the laser nozzle and melt pool in the captured images. Finally, the cladding height is estimated based on the distance between the tip of the laser nozzle and the centroid of the melt pool. The validity of the proposed approach is demonstrated by comparing the inspection results for the cladding height of a horseshoe component with the measurements obtained using a 3-D scanner. The maximum estimation error is found to be just 4.2% Overall, the results confirm that the proposed trinocular vision-based system provides a rapid, convenient and accurate means of determining the cladding height in the DED process.

Flat spiral spring dimension inspection based on machine vision

Dimension inspection plays a vital role in spring quality control. Human inspection is a traditional way to classify the good pieces and not good pieces, which is instable and time consuming. In this paper, a machine vision based inspection system for flat spiral spring dimension inspection is proposed, which can inspect various sizes of flat spiral springs. A robust algorithm is developed to measure the flat spiral spring dimension. Four sets of testing samples are collected to evaluate and compare precision and efficiency between proposed vision system and human inspector. Experimental results show that precision of the proposed machine vision based system is 100%, the time consuming is 0.5 s to 0.7 s for one piece.

A visual inspection system of glass ampoule packaging defects: effect of lighting configurations

ABSTRACTIn any pharmaceutical packaging process, error rates must be kept very low to prevent defective or incorrect medicine from reaching the consumer. Published articles include a focus on the inspection of ampoule content for the presence of contaminants. Methods of inspection include optical cameras that record sequential images to detect the movement of floating objects via feature extraction and classification. Other methods employ spectral imaging and dying of ampoules to detect cracks. Thermal imaging is also used to inspect structural integrity and mechanical stress; however, this approach is marked by relatively high cost. This paper describes the enhancement of an optical vision-based inspection system for a glass ampoule packaging process. The developed solution involves a reconfiguration of the illumination system of the ampoule trays as well as reprogramming of a real-time optical camera. The camera is linked to the process controller and triggers the ejection of trays containing faulty ampoules. Eight defect types are of interest, namely: missing ampoules; ampoules without a label; crushed ampoules; ampoules with a broken tip – present or missing; ampoules with a broken base; cracked ampoules; empty ampoules. The original inspection system performance was inadequate and only detected 2 of the 8 defect types reliably. The improved system which is described in this paper has adequately detected all aforementioned defect types. Experiments show that the overall detection rates have improved from 55% to 99.6% on average. Average camera detection accuracy has improved from 38% to 99.5%. In addition, the false positives, i.e. rejection rates, were reduced from 45% to 7%.

Vision based rail track extraction and monitoring through drone imagery

Abstract Track detection and gauge measurement are considered as crucial aspects of railway inspection systems. Traditionally, monitoring by human inspectors was done. Nowadays, computer vision-based systems are used due to their flexibility and ease of use. In recent years, various prototypes of vision based inspection system have been proposed, most have a camera mounted on carts or trains. Employing drones for such monitoring systems provides a cost effective and accurate means for monitoring tracks. This paper explores the possibilities of computer vision based monitoring through drone imagery. The experimental results ensure that the proposed method provides high reliability and accuracy.

3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capable of detecting and characterizing defects on an airplane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and extract the information about the undesired defects such as dents, protrusions or scratches based on local surface properties. Surface dents and protrusions are identified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Region Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data obtained from an Airbus A320, for different types of defect. The accuracy of the system is evaluated by comparing the measurements of our approach with ground truth measurements obtained by a high-accuracy measuring device. The result shows that our work is robust, effective and promising for industrial applications.

Wood defects classification using laws texture energy measures and supervised learning approach

Machine vision based inspection systems are in great focus nowadays for quality control applications. The proposed work presents a novel approach for classification of wood knot defects for an automated inspection. The proposed technique utilizes gray level co-occurrence matrix and laws texture energy measures as texture feature extractors and feed-forward back-propagation neural network as classifier. The proposed work involves the comparison of gray level co-occurrence matrix based features with laws texture energy measures based features. Firstly it takes contrast, correlation, energy and homogeneity as input parameters to a feed-forward back propagation neural network to predict wood defects and then it take energy calculated from laws texture energy measures based energy maps as input feature to a feed-forward back propagation neural network. Mean Square Error (MSE) for training data is found to be 0.0718 and 90.5% overall average classification accuracy is achieved when laws texture energy measures based features are used as input to the neural network as compared to gray level co-occurrence matrix based input features where MSE for training data is found to be 0.10728 and 84.3% overall average classification accuracy is achieved. The proposed technique shows promising results to classify wood defects using a feed forward back-propagation neural network.

A vision-based character inspection system for tire mold

PurposeThis paper aims to design an automatic inspection system for the characters on tire molds, which involves a vision-based inspection method for the characters on tire molds.Design/methodology/approachAn automatic inspection equipment is designed according to the features of the tire mold. To implement the inspection task, the corresponding image processing methods are designed, including image preprocessing, image mosaic, image locating and character inspection. Image preprocessing mainly contains fitting the contours of the acquired tire mold images and those of the computer aided design (CAD) as the arcs of two circles and polar transformation of the acquired images and the CAD. Then, the authors propose a novel framework to locate the acquired images into the corresponding mosaicked tire mold image. Finally, a character inspection scheme is proposed by combining an support-vector-machine-based character recognition method and a string matching approach. At the stages of image locating and character inspection, image mosaic is simultaneously used to label the defects in the mosaicked tire mold image, which is based on histograms-of-gradients features.FindingsThe experimental results indicate that the designed automatic inspection system can inspect the characters on the tire mold with a high accuracy at a reasonable time consumption.Practical implicationsThe designed automatic inspection system can detect the carving faults for the characters on the tire molds, which are the cases that the characters are wrongly added, deleted or modified on the tire mold.Originality/valueTo the best of the authors’ knowledge, this is the first automatic vision-based inspection system for the characters on tire molds. An inspection equipment is designed and many novel image processing methods are proposed to implement the inspection task. The designed system can be widely applied in the industry.

Image processing based quality control of coated paper folding

Abstract During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks.

Automatic online vision-based inspection system of coupler yoke for freight trains

This article [J. Electron. Imaging 25(6), 061602 (2016), doi: 10.1117/1.JEI.25.6.061602] was retracted on 18 December 2018 due to double publication in this and another peer-reviewed journal. The authors regret this mistake.

Vision Based Inspection System for Missing Footprint Detection on Printed Circuit Boards

Vision Based Inspection System for Missing Footprint Detection on Printed Circuit Boards

Machine vision based characterization of particle shape and asphalt coating in Reclaimed Asphalt Pavement

Reclaimed Asphalt Pavement (RAP) particles are created from the impact removal and/or reprocessing of existing asphalt layers. RAP particles contain a combination of asphalt and aggregates with varying degrees of coating and morphology. Particle size and shape properties, amount of asphalt coating the RAP particles, and the binder content of the RAP are among the important engineering properties that control the performance of this material. This paper introduces an innovative machine vision-based inspection system to quantify the percentage of asphalt coating in different RAP aggregate sources. The Enhanced-University of Illinois Aggregate Image Analyzer (E-UIAIA) is used to acquire the color RGB images of RAP particles from six different sources with sizes between 1/4 in. (6.35 mm) and 1/2 in. (12.5 mm). The influence of asphalt coating percentage on the RAP particle size and shape properties are quantified in this paper. Then, using the advanced color image thresholding scheme incorporated in the E-UIAIA, the corresponding segmented binary images of RAP particles are generated. A newly defined image mean property is used as an automatic variable threshold limit to segment the bright areas in the associated grayscale version of RAP images to detect the uncoated areas on each particle. A relationship was found between the results of the proposed image processing technique in terms of asphalt coating percentages and the asphalt content of the RAP. Furthermore, the asphalt surface coating percentages could be successfully correlated to the fracture energies of concrete specimens containing these RAP particles blended with other virgin aggregates.

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

AUTOMATED INSPECTION OF ASSEMBLY PARTS USING MACHINE VISION

Automated inspection has become an essential requirement in automated manufacturing system. The advances in computer vision and image processing contributed in enhancing developing better vision-based inspection systems that enhanced the efficiency of automated manufacturing systems. In this paper, a new vision-based inspection model for automatic inspection of assembly parts is presented. The developed model can perform different inspection functions including: measurement, counting, checking the presence of part/ features, assembly direction and proper surface coating. The model receives the part images as an input and automatically generates the inspection results and the acceptance or removal of the part either for rework or rejection.

ACF를 이용한 COG 접합 공정에서 도전볼의 음영비와 접촉 저항과의 관계

Chip on glass (COG) bonding using anisotropic conductive film (ACF) is a key technology to assemble a driver IC onto a LCD glass panel. In this paper, an experimental investigation was conducted to investigate the correlation between contact resistance and characteristics of image taken by machine vision based inspection system. The results show that the contact resistance was strongly influenced by the contrast ratio of conductive particle rather than the number of conductive particles. Also, number of conductive particles whose contrast ratio is below 0.75 is crucial for determining the quality of the assembled samples. On the other hand, in the result of high temperature high humidity storage test, the contrast ratio of samples was increased. However, in the case of open-circuit samples after temperature humidity storage test, the number of conductive particles whose contrast ratio is above 0.75 was more than that of the closed-circuit samples.

Deposition uniformity inspection in IC wafer surface

This paper focuses on the task of automatic visual inspection of color uniformity on the surface of integrated circuits (IC) wafers arising from the layering process. The oxide thickness uniformity within a given wafer with a desired target thickness is of great importance for modern semiconductor circuits with small oxide thickness. The non-uniform chemical vapor deposition (CVD) on a wafer surface will proceed to fail testing in Wafer Acceptance Test (WAT). Early detection of non-uniform deposition in a wafer surface can reduce material waste and improve production yields. The fastest and most low-priced inspection method is a machine vision-based inspection system. In this paper, the proposed visual inspection system is based on the color representations which were reflected from wafer surface. The regions of non-uniform deposition present different colors from the uniform background in a wafer surface. The proposed inspection technique first learns the color data via color space transformation from uniform deposition of normal wafer surfaces. The individual small region statistical comparison scheme then proceeds to the testing wafers. Experimental results show that the proposed method can effectively detect the non-uniform deposition regions on the wafer surface. The inspection time of the deposited wafers is quite compatible with the atmospheric pressure CVD time.

Automated Vision Inspection System for a Plastic Injection Mould Component

This paper presents an automated vision based defect inspection and sorting system for a plastic injection mould component called a Retractor Retaining Bush, which is an automotive safety critical component. The system identifies defects which usually occur in a plastic injection mould component. Various types of sensors and actuators were interfaced with the vision hardware and the part handling mechanism, to complete the total automated vision based inspection system.

Defect inspection system for steel wire rods produced by hot rolling process

A vision-based inspection system has been investigated in order to improve the quality of products and processes found in various industries. In this paper, we propose a new defect detection algorithm for steel wire rods produced by the hot rolling process. Because the steel wire rods are long cylinder rods with a circular cross section, the brightness at the sides and center is inconsistent. Moreover, the various types of steel wire rods and the presence of scales affect the reflection properties of the rod surface. In order to resolve the abovementioned difficulties, the use of dynamic programming and a discrete wavelet transform are proposed. An adaptive local binarization method is used to further reduce the effects of scale. The effectiveness of the proposed method is shown by means of experiments conducted on images of steel wire rods that were obtained from an actual steel production line.

Machine Vision Inspection of <i>In Vitro</i> Diagnostic Kits

A machine vision-based inspection system was proposed to inspect the defects of in-vitro diagnostic kits in the production line. The proposed system consisted of two sub-systems, which inspect the strip surface defects and strip assembly defects respectively. The procedure to inspect five types of major defects was determined by the application of image processing and analysis techniques such as image enhancement, edge detection, threshold segmentation, and morphology. The proposed system was implemented using 300 defect samples. Experimental results show the proposed system is effective and efficient.

Integrated Machine Vision을 이용한 LED Die Bonder 검사시스템

LED 칩 패키징에서 다이 본딩은 분할된 칩을 리드 프레임에 고정시켜 칩이 이후 공정을 견딜 수 있도록 충분한 강도를 제공하는 중요한 공정이다. 다이본딩 공정 중에서, 측정 단계는 정확한 에폭시 토출 위치 지정과 충분한 강도를 가지고 접합할 수 있도록 다이가 정확한 위치에 놓여있는지 상태를 결정하는데 있어 매우 중요하다. 본 연구에서는 LED 다이 본딩을 위한 머신 비전 기반의 측정 시스템을 개발하였다. 제안된 시스템에서 에폭시 토출과 어태칭 상태 검출을 위해 각각 2개의 카메라를 사용하였다. 제안된 측정 시스템에 새로운 비전 알고리즘을 적용하였고, 실험을 통해 본 알고리즘의 효율을 검증하였다. 비전 알고리즘을 이용하여 측정된 위치 오차는 <TEX>$X:-29{\mu}m$</TEX>, <TEX>$Y:-32{\mu}m$</TEX>, 회전오차는 3도 이내 인 것을 확인할 수 있었다. 결론적으로 제안된 머신 비전 기반의 측정 시스템을 통해 개발된 다이 본딩 시스템의 향상된 성능을 확인하였다. In LED chip packaging, die bonding is a very important process which fixes the LED chip on the lead flame to provide enough strength for the next process. During the process, inspection processes are very important to detect exact locations of dispensed epoxy dots and to determine bonding status of dies whether they are lies at exact positions with sufficient bonding strength. In this study, a useful machine vision based inspection system is proposed for the LED die bonder. In the proposed system, 2 cameras are used for epoxy dot position detection and 2 cameras are sued for die attaching status determination. New vision processing algorithm is proposed, and its efficiency is verified through required field experiments. Measured position error is less than <TEX>$X:-29{\mu}m$</TEX>, <TEX>$Y:-32{\mu}m$</TEX> and rotation error:<TEX>$3^{\circ}$</TEX> using proposed vision algorithm. It is concluded that the proposed machine vision based inspection system can be successfully implemented on the developed die bonding system.

머신비전에 의한 LED Chip Package 형광물질 토출형상 측정

본 연구는, LED 칩 패키지에 있는 토출된 형광체 수지의 모양을 인라인 측정을 통해 개발된 검사 시스템을 기초로 한 효율적인 머신 비전에 관한 연구이다. 형광체의 반투명 특성 때문에 조사된 빛이 칩의 표면뿐만 아니라 하단 부에서도 반사된다. 이러한 현상이 LED 칩 검사의 신뢰성을 저하시키기 때문에 적절한 조명 광학계를 결정하기 위해 백색광 LED 와 635nm의 레이저 슬릿 빛을 이용하여 검사하였다. 또한, 광 삼각측정법을 이용해 정반사와 분산반사법으로 검사를 수행하였다. 실험 결과 백색 슬릿 광과 정반사 반사법의 조합이 가장 좋은 검사 결과를 낸다는 것을 확인할 수 있었다. Catmull-Rom 스플라인 보간법을 이용하여 측정된 데이터를 부드러운 표면 형상으로 나타내었다. 측정 결과를 통해 개발된 시스템이 LED 칩 패키징 공정에 인라인 검사에 성공적으로 적용될 수 있다는 결론을 내릴 수 있다. In this study, an efficient machine vision based inspection system is developed for the in-line measurement of phosphor resin dispensing shapes on LED chip package. Since the phosphor resin (target material) has semitransparent characteristics, illuminated light beam is reflected from the bottom of the chip as well as from the surface. Since such phenomenon can deteriorate inspection reliability, a white LED and a 635nm laser slit beams are experimentally tested to decide suitable illumination optics. Also, specular and diffuse reflection methods are tested to decide suitable optical triangulation. As a result, it can be known that the combination of a white slit beam source and specular reflection method show the best inspection results. The Catmull-Rom spline interpolation is applied to the obtained data to form smoother surface. From the results, it can be conclude that the developed system can be sucessfully applied to the in-line inspection of LED chip packaging process.