Passive component inspection using machine vision

Du, Winncy Y.; Dickerson, Stephen L.

doi:10.1109/icmcm.1998.670758

Cited by 8 publications

(6 citation statements)

References 3 publications

(1 reference statement)

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“…Therefore, a reliable automatic image inspection system must be developed. Many researches on machine vision inspection have been published [1,2]. However, the requirement of a fast and robust MLCC inspection method is still not satisfied.…”

Section: Introductionmentioning

confidence: 99%

Image Inspection System for Defect Detection of Multilayer Ceramic Capacitors

Tseng

Lai

Lee

2006

2006 International Conference on Intelligent Information Hiding and Multimedia

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In today's competitive passive components market, the quality and delivery time of products are the key factors to survive. The traditional quality control process is performed by human experts, which is slow and error prone. In recent years, automatic inspection becomes a mainstream and many works have been published. However, the industry still demands a faster and more accurate inspection method. In this paper, a novel image inspection algorithm to detect defects of Multilayer Ceramic Capacitor (MLCC) is proposed. A testing system is developed and integrated into a production line. In our experiment, the proposed algorithm is proved to be very effective. The inspection system speeds up the testing process 2.5 times as well as increases the yield rate considerably.

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Section: Introductionmentioning

confidence: 99%

Image Inspection System for Defect Detection of Multilayer Ceramic Capacitors

Tseng

Lai

Lee

2006

2006 International Conference on Intelligent Information Hiding and Multimedia

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“…The results show that the vision system has good inspection accuracy. Du et al [ 14 ] proposed a fast and effective method for detecting passive components and providing complete measurements and sufficient accuracy in automatically detecting the physical characteristics of passive components. Kewei et al [ 15 ] proposed a calibration-based method for optical planimetry.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Liu

Wang

et al. 2022

Micromachines

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The conductive slip ring is used for power or signal transmission between two objects rotating relative to each other. It has become an essential part of modern industrial development. In traditional automated production measurements, the typical method is to use calipers, goniometers, or angle gauges to measure a parameter of the workpiece several times and then average it. These inspection means have low measurement accuracy and slow measurement speed, and measurement data cannot be processed in a timely manner. A machine vision-based method for measuring and controlling the angle of the brushes is proposed for this problem. First, the brush angle forming device was built for the conductive slip ring brush wire, forming the principle and rebound characteristics. Then, machine vision and image processing algorithms were applied to measure the key parts of the conductive slip ring brushes. The data of the forming angle value and rebound angle value were obtained during the forming process of the brush wire angle. Finally, a pre-compensation model for the brush filament rebound was developed and validated based on the curve fitting method. The test results show that the error of the angle measurement is within 0.05°. The average error of the measured rebound angle and the calculated rebound angle of the brush filament pre-compensation model was 0.112°, which verifies the correctness of the pre-compensation model. The forming angle can be controlled more precisely, and the contact performance between the brush wire and the ring body can be improved effectively. This method has the potential to be extended to engineering applications.

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“…In the light and slim products, high precision ceramic components play a critical role. As the result, automatic image detection packing systems [1,2] are employed on production line to ensure that every component is qualified.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection of Skewed Images for Multilayer Ceramic Capacitors

Tseng

Liao

2009

2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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In this paper, we utilized machine vision and image processing to develop an image detection flow for the dimension and appearance of multilayer ceramic capacitors (MLCC), and also used proposed automatic optical inspection (AOI) system in the MLCC production line operation. We compared the advantages and disadvantages of Hough Transform and Histogram analysis.The proposed tiny passive components detection flow can execute the defect detection of each capacitor in real time as soon as the image information of the component is obtained. If defect can be found on the detection flow, it can be immediately judged and classified into defective components, and detection time of each component can be significantly reduced.

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Passive component inspection using machine vision

Cited by 8 publications

References 3 publications

Image Inspection System for Defect Detection of Multilayer Ceramic Capacitors

Image Inspection System for Defect Detection of Multilayer Ceramic Capacitors

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Defect Detection of Skewed Images for Multilayer Ceramic Capacitors

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