• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.245-250
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• 2003

Today, automobile world market is highly competitive. In order to strengthen the competitiveness, quality of automobile is recognized as important and efforts are being made to improve the quality of manufactured components. The directional ability of automobile has influenced on driver directly and hence it must be solved on the preferential basis. In the present research an automated vision system has been developed th inspect the front chassis module. To interpret the inspection data obtained for front chassis module, new interpreting algorithm have been developed. Previously the control of tolerance front chassis module was done manually. With the help of the new algorithm developed, the dimension is calculated automatically to check whether the front chassis module is within the tolerance limit or not.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.253-255
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• 2007

The increasing requirement for consistency and quality in the automotive industry started the development of a Machine Vision Inspection System (MVIS) for a car seat bottom cushion frame with the goal of providing a higher precision Inspection System with minimal components and less human intervention. The modifications made to an existing PC-based MVIS were shown to improve the accuracy and precision of the system. By using four monochrome cameras, the working distance was lowered and the image distortions were lessened without resorting to extensive image processing. The inspection scripts were evaluated if it could recognize good and bad products and were shown to be robust and able to reach an acceptable level of precision. It was also shown that the amount of human interaction was lessened.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.453-461
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• 2008

In this paper, new inspection method is proposed for the surfaces of lead frame and IC's. Optimal optical system and the accurate algorithm for the surface inspection are needed in machine vision area. The proposed optical system is composed of rectangular oblique light illumination and coaxial light illumination for the higher contrast and the results shows the better performances through experiments. The markings of IC surface are inspected using the accurate proposed method using the partitioned correlation coefficient, and the result shows reduction of under kill ratio compared to the previous method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1072-1080
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• 2013

In a the present day, many vision inspection techniques are used in productive industrial areas. In particular, in the semiconductor industry the vision inspection system for wafers is a very important system. Also, inspection techniques for semiconductor wafer production are required to ensure high precision and fast inspection. In order to achieve these objectives, parallel processing of the inspection algorithm is essentially needed. In this paper, we propose the GPU (Graphical Processing Unit)-based parallel processing algorithm for the fast inspection of semiconductor wafers. The proposed algorithm is implemented on GPU boards made by NVIDIA Company. The defect detection performance of the proposed algorithm implemented on the GPU is the same as if by a single CPU, but the execution time of the proposed method is about 210 times faster than the one with a single CPU.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.123-124
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• 2008

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. Therefore we proposes a inspection system in this paper. it will be able to secure the objectivity of the prosecuting attorney using inspection system. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.903-904
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• 2006

This paper is about a vision system that exhibits automatic examination of the conditions of shaft's worm. The system is composed of three part : image acquisition, vision algorithm, and user interface. The image acquisition part is composed of motor control, illumination and optics. The vision algorithm examines the parts using shaft image. User interface is divided into two parts, user interface for feature registering with control value settings and user interface for examination operation. The automatic inspection system introduced in this paper can be used as a tool for final examination of shaft worm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.423-424
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• 2011

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.14-23
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• 2007

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. However, in this paper, multipass tracking more than single pass tracking is performed by conventional seam tracking algorithm and developed one. And tracking performances of two algorithm are compared in multipass tracking. As the result, tracking performance in multi-pass welding shows superior conventional seam tracking algorithm to developed one.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.389-390
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• 2006

The goal of this paper is automation of a remocon inspection process using machine vision system. This system prevents error that is occurred by physical and spirit condition of human. Also this system has been developed to raise the reliability of remocon inspection. This system has been developed only using PC, CCD Camera and Visual C++ for universal workplaces. The performance of this system is an accuracy improvement of $2{\sim}3[%]$ and a processing time reduction of about 100[ms] against existing pattern matching method.