A robust vision inspection system for detecting surface defects of film capacitors

Yang, Yuxiang; Zha, Zheng-Jun; Gao, Mingyu; He, Zhiwei

doi:10.1016/j.sigpro.2015.10.028

Cited by 41 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Minor defects were also included in separate category. [290] IR-CUT filter defects such as stain, scratch, and edge crack [296] Surface defects in micro multi-layer nonspherical lens module of CMOS such as bright spot, dark spot, scratch, foreign material and hole [297] Compact camera lens and spacer ring defects such as stain, bright dot, scratch, pit, and scar Passive Components [291], [292] Ripple defects in the surface barrier Layer chips of ceramic capacitors [298] Tiny surface defects in the surface barrier Layer chips of passive electronic components [299] Surface defects of film capacitors Thermal Fuse [293] Bur, black dot, small-head, and flake defects and lightning setup, computer (processor), conveyor and sorting mechanism as shown in Figure 16. The illumination is responsible for providing constant/customized lightning conditions.…”

Section: Image Acquisition Technologies -Hardware Systemsmentioning

confidence: 99%

“…Zhao et al in [312] proposed a similar stereo vision system using two cameras to inspect the pins on multi-type electrical connectors of the components. Because each film capacitor has six surfaces to be detected, Yang et al in [299] used four image acquisition systems that are adjusted to detect the surface defects of capacitors. Each camera gets the image information of two surfaces from side angle, as shown in Figure 28.…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

“…Park and Kweon in [234] proposed four image FIGURE 28. Imaging system setup considered by Yang et al in [299] acquisition systems as well that consisted of four different combinations of camera and LED sources with different orientation angles for inspecting AMOLED defects as shown in Figure 29. The multiple imaging systems with different orientation angles are used for acquiring images for the same component multiple times such that the defective features are detected from at least one of the four images.…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

“…However, the recall and precision rates for classification results were considerably low. Yang et al in [299] combined adaptive thresholding with a frequency filtering technique called Non-subsampled Contourlet Transform (NSCT) to inspect surface defects of film capacitors. The NSCT was developed from the wavelet theory as a multi-scale geometric analysis method and was chosen due to its ability in describing the edge contours of film capacitor defects.…”

Section: ) Segmentationmentioning

confidence: 99%

See 3 more Smart Citations

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Ebayyeh

Mousavi

2020

IEEE Access

View full text Add to dashboard Cite

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

show abstract

Section: Image Acquisition Technologies -Hardware Systemsmentioning

confidence: 99%

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

Section: ) Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Ebayyeh

Mousavi

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Besides, limited by the human errors, the manual grouping method is prone to false groupings. In the past few years, with the rapid development of automation technology, more and more factories have applied automation technology to replace the traditional manual method for higher efficiency and lower costs [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Yang

Gao

et al. 2017

Energies

Self Cite

View full text Add to dashboard Cite

Abstract:The inconsistent characteristics of individual power batteries in a battery pack can seriously affect the performance and service life of the whole pack. Battery grouping is an effective approach for dealing with the inconsistency problem by grouping batteries with similar characteristics in the same battery pack. In actual production, the battery grouping process still relies on the traditional manual method, which results in high labor and time costs. In this paper, a robust and effective battery grouping method based on the characteristic distribution model is developed. Specifically, a novel characteristic distribution model is proposed to determine the grouping priority of different batteries. Then, an improved k-nearest-neighbor algorithm is used to decide which batteries should be group into the same battery pack. Experimental results demonstrate the effectiveness of the proposed method.

show abstract

A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface

Ali

Lun

2018

Int J Adv Manuf Technol

View full text Add to dashboard Cite

A robust vision inspection system for detecting surface defects of film capacitors

Cited by 41 publications

References 21 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface

Contact Info

Product

Resources

About