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Author: search.filters.author.Kunakornvong, PichateHas files: No

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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.
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    Micro Defect Detection on Air-Bearing Surface
    (2015-03-27)
    Kunakornvong, Pichate
    ;
    Sooraksa, Pitikhate
    This chapter presents texture analysis methods for detection of contaminations (micro particle, stain and metal) on the air-bearing surface (ABS). A complete system is developed composed of an image acquisition module, a feature extraction module and a decision-making module. The input ABS image is first analyzed by the texture unit and the co-occurrence matrix to obtain texture features which are then transformed by the principle component analysis (PCA) for effective classification of the defective samples. The chapter detects the contamination particle based on the theory of light scattering technologies: laser, detector and optic. Most light scattering techniques are used for counting particles. J. L. Blesener studied the non-imaging laser particle counter (LPC) for detection of a single particle. B. Bhushan utilized LPC instruments and sampling techniques for detecting and determining the size of particle contamination in rigid disk drives. S. Kochevar proposed the next generation of contamination monitoring using nanotechnology.
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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.
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    Rapid detection of hairline cracks on the surface of piezoelectric ceramics
    (2013-02-01)
    Withayachumnankul, Withawat
    ;
    Kunakornvong, Pichate
    ;
    Asavathongkul, Channarong
    ;
    Sooraksa, Pitikhate
    Lead zirconate titanate, also known as PZT, is a type of piezoelectric ceramics commonly used for actuators in modern hard disk drives (HDDs). These PZT actuators are prone to hairline surface cracks, prompting detection and removal during the HDD production. Machine vision is then utilized for automatic detection of these cracks. The developed image processing approach comprises three steps: extraction of the region of interest, enhancement of crack regions, and elimination of irrelevant features. The key step, crack region enhancement, employs image filtering with a specifically designed filter kernel, capable of extracting thin crack regions from the rough surface of PZT actuators. The experiments show that the algorithm reveals cracks with high accuracy and high sensitivity, whereas the overall processing time satisfies the industrial environment. © 2012 Springer-Verlag London Limited.
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    Defect detection on air bearing surface with luminance intensity invariance
    (2012-12-01)
    Kunakornvong, Pichate
    ;
    Tangkongkiet, Chiewchan
    ;
    Sooraksa, Pitikhate
    One important part of a read/write head in hard disk drive is Air Bearing Surface (ABS), therefore its verifying is necessary. Main problem of verifying ABS with machine vision in manufacturing process is variance of luminance intensity that affects image acquisition. This research proposes method for reduce variance of luminance intensity by Texture unit, then extracting the features of ABS with Principle Component Analysis, and selected threshold for clustering defected and non-defected ABS by mean of threshold set which has minimum summation of two types error as false rejection and false acceptance from training data. The experimental results show that without Texture unit, defected and non-defected ABS cannot be clustered. Thus proposed method is better used because it can be reduce variance of luminance intensity. © 2012 IEEE.