Vertical Edge Detection-Based Automatic Optical Inspection of HGA Solder Jet Ball Joint Defects (original) (raw)
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Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly
Journal of Failure Analysis and Prevention
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.
Automated Visual Inspection System for Mass Production of Hard Disk Drive Media
Procedia Engineering, 2012
Manual visual inspection is currently used in the modern Hard Disk assembly process. This research shows a feasible design to automate the visual inspection process based on wavelength dependant detection. Two defect detection algorithms, one -computationally simple and another -complex, are explored in this paper. The developed system meets the performance of the current manual inspection method while providing high accuracy in targeted defects.
IEEE Transactions on Automation Science and Engineering, 2014
Automation or selective automation is adopted as a solution to most productivity problems in the hard disk drive (HDD) industry as the industry continues to grow at a 40% compounded annual growth rate. An automated production line for manufacturing the head gimbal assembly (HGA) has been developed as part of the automation solution. In the automated HGA production line, a solder jet ball (SJB) soldering station connects the suspension circuit to the slider body. We propose a Bayesian approach to automated optical inspection (AOI) of the SJB joint in the HGA process, implementing Tree Augmented Naïve Bayes Network (TAN-BN) plus check classifier in-situ using GeNIe/SMILE within the inspection software. The system is further enhanced with a result checker, achieving an overall accuracy of 91.52% with 660 production parts in a blind test. Note to Practitioners-This paper was motivated by the problem of inspecting for defective solder joints in linear, automated production line for hard disk drive parts. The size and placement of the part in the tool presented a challenge to capturing a full view of the object under inspection. Existing approaches manipulate parts of the image under different conditions. This paper suggests a method that associates the likelihood of a measured feature of the image to the quality of the solder joint produced. In this paper, we characterized the features mathematically and established a probabilistic relationship between the features and the quality of the solder joint. We then showed how the relationship can be used in real-time determination of the quality of a solder joint presented to the inspection system. We showed that the system achieved reasonable accuracy when applied to production. Index Terms-Automated optical inspection (AOI), Bayesian networks, Peter-Clark Bayesian network (PC-BN), solder-joint defect, solder-joint inspection, tree-augmented Naïve Bayesian network (TAN-BN). I. INTRODUCTION AND MOTIVATION T HE CONTINUOUS growth of digital content creation, consumption, and preservation is fueling demand for hard disk drives (HDDs).
Design of automatic vision-based inspection system for solder joint
Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.
Automatic visual solder joint inspection
IEEE Journal on Robotics and Automation, 1985
An approach is described for the automatic inspection of solder joints on printed circuit boards. Common defects are identified in solder joints and a joint is classified as being good or belonging to one of the defective classes. The motivation for this classification is not just the detection of defective joints, but the desire to automatically take corrective action on the assembly line. The features used for classification are based on characteristics of intensity surfaces. It is shown that features derived fromfacets and Gaussian curvature are effective in the classification of solder joints using a minimum-distance classification algorithm. Class separation plots are shown to be useful for quickly studying individual effectiveness of a feature or pair of features in classification. Results show the efficacy of the described approach.
Visual Inspection Technology in the Hard Disc Drive Industry
2015
A presentation of the use of computer vision systems to control manufacturing processes and product quality in the hard disk drive industry. Visual Inspection Technology in the Hard Disk Drive Industry is an application-oriented book borne out of collaborative research with the worlds leading hard disk drive companies. It covers the latest developments and important topics in computer vision technology in hard disk drive manufacturing, as well as offering a glimpse of future technologies.
An Angle Measurement of Hard Disk's Head in the HSA Using Image Processing
2009 International Conference on Computational Intelligence and Software Engineering, 2009
Hard disk's head (HDH) is one of the most important parts in a hard disk. Because the floating height between the HDH and the platter is very narrow, it is necessary to make sure that the HDH's pitch angle and roll angles are from-0.05˚ to 0.05˚ on the Head Stack Assembly (HSA). The maximum error is 0.0226˚ for pitch angle and 0.0288˚ for roll angle according to our experimental results. This paper presents an image processing techniques for measuring the pitch and roll angle of the HDH.
Detection of defects at BGA solder joints by using X-ray imaging
… , 2002. IEEE ICIT'02 …, 2002
In the surface mount technology, a ball grid array (BGA) has been used in the production of PC boards. This paper deals with the detection of defects at BGA solder joints in PC boards by using X-ray imaging. Types of defects at BGA solder joints are solder ...
Automatic optical inspection for detecting defects on printed circuit board inner layers
International Journal of Advanced Manufacturing Technology, 2005
This paper studies automatic optical inspection for detecting defects on the printed circuit board inner layer. The development of this study can be divided into five stages, they are reference image rebuilding, inspection image normalization, image subtraction, defects separation and defect classification. In the image subtraction stage, the difference between the reference image from the printed circuit board design and the inspected image is checked for defects. Each defect region is separated using a defect outer boundary tracing method. A boundary state transition method is proposed to classify the defect types. This system can recognize eight defect types, open, mouse bite, pinhole, missing conductor, short, spur, excess copper and missing hole. In addition, a comparison with the methods described in the literature is made, proving that the proposed method produces better results .