• Title/Summary/Keyword: SMD recognition

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Pattern recognition of SMD IC using wavelet transform and neural network (웨이브렛 변환과 신경회로망을 이용한 SMD IC 패턴인식)

  • 이명길;이준신
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.7
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    • pp.102-111
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    • 1997
  • In this paper, a patern recognition method of surface mount device(SMD) IC using wavelet transform and neural network is proposed. We chose the feature parameter according to the characteristics of coefficient matrix which is obtained from four level discrete wavelet transform (DWT). These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Experimental results show that when the same form of feature pattern, as is used for learning, is put into neural network and gained 100% rate ofrecognition irrespective of SMD IC kinds, location and variation of illumination. In the case of unused feature pattern for learning, the recognition rate is 85.9% under the similar surroundings, where as an average recognition rate is 96.87% for the case of reregulated value of illumination. Proosed method is relatively simple compared with the traditional space domain method in extracting the feature parameter and is also well suited for recognizing the pattern's class, position and existence. It can also shorten the processing tiem better than method extracting feature parameter with the use of discrete cosine transform(DCT) and adapt the surroundings such as variation of illumination, the arrangement and the translation of SMD IC.

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A study on the generation of SMD mounting position and orientation utilizing image processing technique (영상처리 기법을 이용한 SMD 장착위치 및 방향 생성에 관한 연구)

  • 구영모
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.22 no.50
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    • pp.11-21
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    • 1999
  • In this paper, an algorithm to generate the mounting position and orientation of SMD is proposed. For the proposed algorithm, we used the image captured from PCB and utilized image processing technique. Pre-processing technique, threshold level determination method, divided recognition of the SMD pattern on a PCB, calculating mounting position and orientation are related topics of this research. All of the related topics were reviewed and the results of this research were obtained from applying the algorithm to the real Surface Mounting Machine(model:MCUl/CPM) made by Samsung Electronics Co.

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A study on the generation of SMD mounting position and orientation utilizing image processing technique (영상처리 기법을 이용한 SMD 장착위치 및 방향 생성에 관한 연구)

  • 구영모;송경섭;신기범
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.113-116
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    • 1996
  • In this paper, an algorithm to generate the mounting position and orientation of SMD is proposed. For the proposed algorithm, we used the image captured from PCB and utilized image processing technique. Pre-processing technique, threshold level determination method, divided recognition of the SMD pattern on a PCB, calculating mounting position and orientation are related topics of this research. All of the related topics were reviewed and the results of this research were obtained from applying the algorithm to the real Surface Mounting Machine(model: MCU1 /CPM) made by Samsung Electronics Co.(Ltd.)

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A study on pattern recognition using DCT and neural network (DCT와 신경회로망을 이용한 패턴인식에 관한 연구)

  • 이명길;이주신
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.3
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    • pp.481-492
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    • 1997
  • This paper presents an algorithm for recognizing surface mount device(SMD) IC pattern based on the error back propoagation(EBP) neural network and discrete cosine transform(DCT). In this approach, we chose such parameters as frequency, angle, translation and amplitude for the shape informantion of SMD IC, which are calculated from the coefficient matrix of DCT. These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Learning of EBP neural network is carried out until maximum error of the output layer is less then 0.020 and consequently, after the learning of forty thousand times, the maximum error have got to this value. Experimental results show that the rate of recognition is 100% in case of the random pattern taken at a similar circumstance as well as normalized training pattern. It also show that proposed method is not only relatively relatively simple compare with the traditional space domain method in extracting the feature parameter but also able to re recognize the pattern's class, position, and existence.

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A study on the inspection algorithm of FIC device in chip mounter (칩 마운터에의 FIC 부품 인식에 관한 연구)

  • Lyou, Kyoung;Moon, Yun-Shik;Kim, Kyoung-Min;Park, Gwi-Tae
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.3
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    • pp.384-391
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    • 1998
  • When a device is mounted on the PCB, it is impossible to have zero defects due to many unpredictable problems. Among these problems, devices with bent corner leads due to mis-handling and which are not placed at a given point measured along the axis are principal problem in SMT(Surface Mounting Technology). It is obvious that given the complexity of the inspection task, the efficiency of a human inspection is questionable. Thus, new technologies for inspection of SMD(Surface Mounting Device) should be explored. An example of such technologies is the Automated Visual Inspection(AVI), wherein the vision system plays a key role to correct this problem. In implementing vision system, high-speed and high-precision are indispensable for practical purposes. In this paper, a new algorithm based on the Radon transform which uses a projection technique to inspect the FIC(Flat Integrated Circuit) device is proposed. The proposed algorithm is compared with other algorithms by measuring the position error(center and angle) and the processing time for the device image, characterized by line scan camera.

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