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Author: search.filters.author.Sooraksa, PitikhateSubject: search.filters.subject.Defect detection

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    Unified histogram equalization for defect detection on air bearing surfaces
    (2017-02-01)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    Delivery of zero-defect products to customers in due time is key to customer satisfaction. This paper presents a new machine vision system for detecting the defects on the air bearing surface of the head gimbal assembly (HGA). The paper presents two contributions: a practical software implementation by using unified histogram equalization, and a defect detection algorithm with a block matrix technique and texture analysis. In order to test the algorithm with a real-time system, a high speed capsule conveyor was built as a new, fast in-line conveyor for transporting capsules containing HGAs. According to the experimental results, the defect detection was drastically enhanced and the performance of the proposed algorithm was satisfactory for use in a real assembly line. In other words, the visual subsystem was successful at capturing moving parts during image acquisition and at equalizing the acquired image. This new system can be used to replace a slow-speed detection system in order to increase the unit per hour production of an industrial assembly line.
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    A practical low-cost machine vision sensor system for defect classification on air bearing surfaces
    (2017-01-01)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    In this paper, we present a newly adapted machine vision method and a practical low-cost machine vision sensor for defect classification of the air bearing surfaces (ABSs) of a hard disk drive, which controls the flying height of the recording heads moving above a disk in operation. A defective ABS can cause poor reading and writing performance; hence, it is necessary to verify its integrity before assembling it into the final product. The proposed sensor system was designed and implemented to detect defects by an effective combination of image segmentation and block matrix techniques as well as classifying them using an expert system under dark- and bright-field conditions. Our system processes subregions of interest and sub-blocks in parallel so that they can take advantage of multiple processor cores. From the trial runs, the small fractional error and low average processing time suggested that our proposed system is effective and can be used in an industrial assembly line.
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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.