Rapid detection of hairline cracks on the surface of piezoelectric ceramics

Withayachumnankul, Withawat; Kunakornvong, Pichate; Asavathongkul, Channarong; Sooraksa, Pitikhate

doi:10.1007/s00170-012-4085-4

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Each member in the m × n image block represented an average intensity of each subblock, calculated using Eqs. (11) and (12).…”

Section: Generation Of a Block Matrixmentioning

confidence: 99%

“…Texture (7) Co-occurrence matrix Textile (8) Principle component analysis Wood (9) Mean shift Solar wafer (10) Weibull Synthetic aperture radar (SAR) image (11) Structural algorithm Edge detection PZT, (12) Ceramic (13) , Flash thermography (FT) (14) Morphological operation Textile, (15)(16)(17) Ceramic (18) Spectral and filtering algorithm…”

Section: Imaging Systemmentioning

confidence: 99%

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A Practical Low-Cost Machine Vision Sensor System for Defect Classification on Air Bearing Surfaces

2017

Sensors and Materials

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Keywords: air bearing surface, head gimbal assembly, defect detection, machine vision, region of interestIn 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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“…Each member in the m × n image block represented an average intensity of each subblock, calculated using Eqs. (11) and (12).…”

Section: Generation Of a Block Matrixmentioning

confidence: 99%

Section: Imaging Systemmentioning

confidence: 99%

A Practical Low-Cost Machine Vision Sensor System for Defect Classification on Air Bearing Surfaces

2017

Sensors and Materials

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“…GAs provided the highest corrective percentage compared to Sobel and Canny. Withayachumnankul et al [12] designed a filter kernel to detect hairline crack defect edges on the surface of hard disk amplified piezoelectric (PZT) actuators. The kernel filter detected lines at multiple angles.…”

Section: A Previous Workmentioning

confidence: 99%

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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“…There have been several experimental studies on the automatic visual inspection of hard disk defects such as the detection of defects on the HDD media surface using spectral imaging [1], [2]. Withayachumnankul et al [3] devised a filter kernel to detect the edges of hairline crack defects on the surface of piezoelectric actuators. Yammen et al [4] explored the inspection of corrosions on pole tips at the end of an airbearing slider using area-based and contour-based features.…”

Section: Previous Workmentioning

confidence: 99%

Detection of micro contamination in hard disk drives using maximum likelihood estimation and angle detection

Ieamsaard

Muneesawang

Sandnes

2016

2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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Micro contamination is one of the critical defects that occur on the head gimbal assembly (HGA). The HGA is a key component of the read/write assembly of a hard disk drive. This paper presents an image-based automatic inspection method for micro-contamination detection. Maximum likelihood estimation combined with angle measurements are proposed for identifying defects. The performance of the proposed maximum likelihood estimation and angle measurement method is compared to previous angle measurement and intensity thresholding methods. The experimental results show that the fusion of maximum likelihood estimation and angle measurements outperforms the angle measurement and intensity thresholding method with an accuracy of 87.9 % compared the accuracy of 80.1% reported in previous work.

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Rapid detection of hairline cracks on the surface of piezoelectric ceramics

Cited by 17 publications

References 7 publications

A Practical Low-Cost Machine Vision Sensor System for Defect Classification on Air Bearing Surfaces

A Practical Low-Cost Machine Vision Sensor System for Defect Classification on Air Bearing Surfaces

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

Detection of micro contamination in hard disk drives using maximum likelihood estimation and angle detection

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