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    Machine vision for defect detection on the air bearing surface
    (2016-08-16)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    Air Bearing Surface (ABS) is a part on HDD responsible to control the flying height during read/write data. The defect on ABS causes poor performance, therefore it is necessary to verify the part before assembly to the final products. This paper presents a practical approach for machine vision to detect defect on the air bearing surface (ABS) of a head gimble assembly. Detail design on imaging system and contamination detection is provided. The proposed system is designed and implemented to detect the defect on ABS using image segmentation techniques with generation of block matrices. To determine defect in paralleled processing architecture, sub blocks and sub-ROI are allocated and employed. The results show the effectiveness of the designed system which can be deployed in the industrial assembly lines.
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    Defect detection on air bearing surface with gray level co-occurrence matrix
    (2014-01-01)
    Kunakornvong, Pichate
    ;
    Tangkongkiet, Chiewchan
    ;
    Sooraksa, Pitikhate
    Air bearing surface (ABS) is the part of magnetic read/write head flying height controller. It is very important part in magnetic disk (hard disk drive), defected on ABS lead to crash between read/write head and disk surface, therefore its verifying is necessary. The best way to verify defect on ABS is machine vision. Main problem of machine vision in real world is variation of luminance intensity that affects image acquisition. This research proposes method for detecting defect on ABS which has variance luminance intensity, the Co-Occurrence Matrix is used to avoid the variance intensity of ABS image then feature parameter is defined by four identification features and defected detect by threshold that selected from Euclidean distance of each identification. The experimental results show very low error of defect detection on ABS by Co-Occurrence matrix and their identification feature. © 2014 IEEE.