This paper develops an automatic optical inspection system for flying fish egg quality inspection. The automatic optical inspection system consists of a 2-axes stage, a digital camera, a lens, a LED light source, a vacuum generator, a tube and a tray. This system can automatically find the particle on the flying egg tray and used stage to driver the tube onto the particle. Then use straw and vacuum generator to pick up the particle. The system pick rate is about 30 particles per minute.
In order to develop a high accuracy optical alignment system for precision molding machine, a geometric matching method was developed in this paper. The alignment system includes 4 high magnification lenses, 4 CCD cameras and 4 LED light sources. In the precision molding machine, a bottom metal mold and a top glass mold are used to produce a micro lens. The two molds combination does not use any pin or alignment part. They only use the optical alignment system to alignment. In this optical alignment system, the off-axis alignment method was used. The alignment accuracy of the alignment system is about 0.5 μm. There are 2 cross marks on the top glass mold and 2 cross marks on the bottom metal mod. In this paper did not use edge detection to recognize the mask center because the mask easy wears when the combination times increased. Therefore, this paper develops a geometric matching method to recognize mask center.
In recent years, the glasses had gradually been personal accessory to human life, so the demand of various types of glasses has increased significantly, especially safe glasses and sunglasses. And the requirement of full-inspection of the lens of safe glasses and sunglasses are getting seriously. In the past, the fast lens optical quality inspection where performed by Ronchi test and the Ronchigram images were observed and judged by human eyes. However, the larger uncertainty of measurement will be induced while observing the Ronchi patterns using human eyes. Therefore, this study presents the development of an automatic lens Inspection Instrument based on Ronchi tester, which comprises of the machine vision, image analysis and processing technique without human operation involved. In addition, an optical quality index based on Ronchigram has been developed so as to classify the quality of test lens. In this paper, we propose a lens quality index (LQI) to evaluate the optical quality of lens to be inspected.
In this paper, 2 sub machine vision based alignment systems were used to establish a high speed alignment system for screen printing. It can be used on the solar cell and flat display panel manufacture. The 2 sub alignment system can auto align target simultaneously. When one target was takes out, another target can implement auto alignment simultaneously. It can save the wait time for target take out procedure. The sub alignment system includes 4 CCD cameras, 4 lens, 4 outer coaxial LED light sources, a vacuum table and a 3 axis motorized stage. The alignment accuracy is about 1 μm.
The core of the power inductor is made by powder metallurgy. By its nature, the powder-formed part has inherent nonuniform porosity pattern and parallel tool marks on the metal surface. In the past, the surface inspection of core is usually performed by using human eyes. However, the larger uncertainty of inspection will be induced while observing the defect image using human eyes. In the automated optical inspection process, the feature of defect is not easily separated from the image background by using the simple binarization method. This study develops an image processing method and employs a uniform diffuse illumination to build up a surface defect inspection system. Experiment result shows the distinguish rate is 95.5%, therefore it is clear that this system can successfully detects a set defect of the core of inductor.
An automatic scanning path generation method is developed. The method is based on a 3-axis automatic inspection
system which is used to detect the clearance ratio of spinneret plate. The user can rely on this method to automatically
generate the scanning path for an unknown spinneret plate in the spinneret test. Then the scanning path can be learned by
the inspection system and repeated it for other the same spinneret. Two type spinnerets are introduced in this paper to
describe the automatic scanning path generation method. In this paper, the 3-axis automatic inspection system includes a
3-axes motorized linear stage, a telcentric lens, a top light source, a bottom light source, 1 CCD camera and a controlled
A multi-function lens test instrument is report in this paper. This system can evaluate the image resolution, image
quality, depth of field, image distortion and light intensity distribution of the tested lens by changing the tested patterns.
This system consists of a tested lens, a CCD camera, a linear motorized stage, a system fixture, an observer LCD
monitor, and a notebook for pattern providing. The LCD monitor displays a serious of specified tested patterns sent by
the notebook. Then each displayed pattern goes through the tested lens and images in the CCD camera sensor.
Consequently, the system can evaluate the performance of the tested lens by analyzing the image of CCD camera with
special designed software. The major advantage of this system is that it can complete whole test quickly without
interruption due to part replacement, because the tested patterns are statically displayed on monitor and controlled by the
To improve upon and provide a methodological alternative to published optoelectronic angle measurement systems, the paper performs high-accuracy angle measurement by use of laser diodes, dual-axis position sensing detectors, and a series of reflections between two first-surface mirrors. Measurement accuracy improves from 0.5 to 0.05 arc sec as the light ray is reflected back and forth several times between the mirrors. Analytic ray tracing is used to model the reflected light ray so as to determine the system equations implicitly in terms of the measured angles. The first-order Taylor series expansion provides a linear form of the system equations. To validate the system, a prototype is built. Calibration and stability experiments are performed. Experimental results show the resolution, accuracy, and measurement range are, respectively, 0.008, 0.05, and ±250 arc sec. Compared with traditional optical angle measurement systems, this method has many merits such as low cost, simple configuration, high sensitivity, and high linearity.
In this paper, we present a printed circuit board (PCB) inspection system based on using Hausdorff distance for image alignment and defect detection. In addition, we apply support vector machine (SVM) for the defect classification and the metal classification in this system. The three major components in the proposed PCB inspection system consist of image alignment, defect detection, and defect classification. In image alignment, a coarse-to-fine search technique is applied to accelerate the speed of finding the minimal Hausdorff distance between the reference and the inspection images. For defect detection, we calculate the Hausdorff distance of every pixel in the inspection image as the first step and compare the result with a predefined threshold. For the cases where the computed Hausdorff distance is greater than the threshold, the location of that pixel is labeled as a defect suspect. The existence of defect then can be confirmed by merging the nearby suspects into one object. For defect classification, the local image features are extracted and passed to support vector machine for training and identifying defect types. In this work, we focus on distinguishing the type of a defect as one of open, short, pinhole, over-etch, or under-etch types. Support vector machine can be applied to metal classification as well. At the current stage, we supply support vector machine with RGB color information as the feature vector for metal classification. Experimental results show that the Hausdorff distance based method detects defects in a printed circuit board efficiently and accurately, and the support vector machine approach also gives satisfactory results for both defect and metal classifications.
In this paper, we propose a coarse-to-fine image comparison algorithm based on Hausdorff distance for PCB inspection. The Hausdorff distance can be used in a geometrics-based inspection framework for comparing binary edge maps extracted from the inspection images. To use the Hausdorff distance for image alignment, we need to compute the edge map from the input image as the first step. In some cases, one may use directed Hausdorff distance as a similarity measure in order to reduce the computational cost during the image alignment. Moreover, a modified version of directed Hausdorff distance is employed to enforce robustness against random noises introduced by edge detection. The search for the optimal alignment by minimizing the associated Hausdorff distance is accomplished by an efficient multi-resolutional downhill simplex search algorithm. In addition to the image alignment, we also apply a modified Hausdorff distance to detect defects in PCB. In our inspection system, we apply the partial Hausdorff distance in a local circuit window to reduce the inspection area dramatically, thus making it very efficient for PCB inspection. Experimental results on some PCB inspection examples are shown to demonstrate the accuracy and efficiency of the proposed Hausdorff-distance based inspection system.