Automated Optical Inspection for Solder Jet Ball Joint Defects in the Head Gimbal Assembly Process

Ieamsaard, Jirarat; Fuangpian, Thanapoom

doi:10.1002/9781119058755.ch4

Cited by 2 publications

(3 citation statements)

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“…Comparatively, there are fewer documented studies on the specific problem of HGA jet bonding defect detection. Ieamsaard and Fuangpian [4] proposed a visual inspection system using morphology and template matching for solder ball bridging ball defect detection and solder ball incomplete defect detection and compared the results to those obtained using a chain-code descriptor-based method. The morphology and template matching method provided 99.0% accuracy for both solder ball bridging and solder ball incomplete defect detection, respectively.…”

Section: A Previous Workmentioning

confidence: 99%

“…The chain-code descriptor-based method provided an accuracy of 88.4% for solder bridging detection and 85.6% for solder ball incomplete detection. Kmean clustering was used for missing solder ball defect detection [4] to detect the contour of each pad. The complete contours forming loops were counted and finally classified.…”

Section: A Previous Workmentioning

confidence: 99%

“…The method achieved a high accuracy of 99.6%. A Sobelbased vertical edge detection approach was also proposed for solder ball burning defects detection [4]. However, the performance of the Sobel-based method depends on the quality of the test image as false detections are easily caused by reflections due to the illumination.…”

Section: A Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Ieamsaard

Muneesawang

Sandnes

2018

J Fail. Anal. and Preven.

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The detection of low quality solder joint quality in hard disk drive (HDD) manufacturing is a time consuming, error-prone and costly process that is often performed manually. This paper thus proposes two automated optical solder jet ball joint defect inspection methods for head gimbal assembly (HGA) production. The first method uses a Support Vector Machine (SVM) for fault detection and the second method uses vertical edge detection to identify solder ball and pad burning defects. The methods were tested with 5,530 HGA images, and their performance was compared to a Bayesian-based method. Experimental results show that the vertical edge detection method gave the best results, with an under reject rate of 0.75% and an over reject rate of 1.88%. The accuracy of the vertical edge detection method was 98.2%, which is higher than the accuracy of 89.9% for the Bayesian-based method, and 84.6% for the SVM-based method. Index Terms-optical inspection; solder jet ball joint defect; vertical edge detection; HDD manufacture

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Section: A Previous Workmentioning

confidence: 99%

Section: A Previous Workmentioning

confidence: 99%