Visual inspection system for the classification of solder joints

Kim, Tae-Hyeon; Cho, Tai-Hoon; Moon, Young Shik; Park, Sung Han

doi:10.1016/s0031-3203(98)00103-4

Cited by 85 publications

(36 citation statements)

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“…It needs different types of algorithms because of the circular solder shape and the pin. Some solutions for this kind of inspection are reported in [127]- [129]. A summary of possible failures and appliances that can detect them are shown in Table V.…”

Section: Post Soldering Inspectionmentioning

confidence: 99%

Automatic Optical Inspection of Soldering

Janóczki¹,

Becker²,

Jakab³

et al. 2013

Materials Science - Advanced Topics

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Section: Post Soldering Inspectionmentioning

confidence: 99%

Automatic Optical Inspection of Soldering

Janóczki¹,

Becker²,

Jakab³

et al. 2013

Materials Science - Advanced Topics

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“…After this selection, a region of interest is cropped for further processing. Finally, average values of pixels in the cropped images are computed as follows [4]:…”

Section: Feature Extractionmentioning

confidence: 99%

“…A feature is a value describing an object in a numerical form, and the selection of good features is critical to the success of any classification algorithm. Generally, 2D features are computationally simpler than 3Dfeatures [4]. Efficient techniques for solder joint inspection have been described using three layers of ring-shaped light-emitting diodes (LEDs) with different illumination angles; three frames of images were sequentially obtained, the regions segmented, and then the solder joints classified using a fuzzy membership function and neural network classifier [4].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, 2D features are computationally simpler than 3Dfeatures [4]. Efficient techniques for solder joint inspection have been described using three layers of ring-shaped light-emitting diodes (LEDs) with different illumination angles; three frames of images were sequentially obtained, the regions segmented, and then the solder joints classified using a fuzzy membership function and neural network classifier [4]. Jagannathan [5] developed a new system for the intelligent machine vision inspection of wave solder joints.…”

Section: Introductionmentioning

confidence: 99%

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Vision inspection system for the identification and classification of defects in MIG welding joints

Kumar

Natarajan

Ananthan

2011

Int J Adv Manuf Technol

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The variety of vision inspection systems for welding defects in the present manufacturing scenario is needed for overcoming certain limitations such as the problem of inaccuracy in the images, non-uniformed illumination, noise and deficient contrast, and confusion in defects if they occur in the same spot at the surface and subsurface. Hence, it is imperative to design a new vision inspection system which will enable to overcome the aforementioned problems in welding. A sophisticated new vision inspection system using machine vision has been developed for this study to identify and classify the surface defects of butt joint as per standard EN25817 in metal inert gas (MIG) welding. In this proposed vision system, images of welding surfaces are captured through a CCD camera. Four frames of sequence of images are obtained using four zones of LEDs using the front light illumination system in this method. From these images, the regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated. The same process can be extended further to four zones (four quadrants) of four types of welded joints. Finally, welded joints can be classified into one of the four predefined ones based on the back-propagation neural network. The proposed system demonstrates an overall accuracy of 95% from the 80 real samples tested.

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“…However, the high cost, low throughput and sampling loss of the above approaches call for research on non-destructive machine vision systems. Several optical inspection systems for solder joints have been reported (Bartlet et al, 1988;Capson & Eng, 1988; T-H. Kim et al, 1999;Ryu & Cho, 1997), using different illumination techniques and defect classification schemes. There have also been efforts to evaluate data fusion to combine data from various sensors for quality inspection of soldering processes (Lacey et al, 1993).…”

Section: Introductionmentioning

confidence: 99%