Vision System for Reading Serial Numbers of Hard Disk Drive Slider Bar and Barcode on Its Fixture
Keywords:
Machine vision system, Image processing, Autofocus, Liquid LensAbstract
Part traceability is one of the inevitable requirements in most modern manufacturing processes. In a hard disk drive (HDD), magnetic head dictates the performance of read/write operations. A number of manufacturing processes are involved to transform a wafer disc to slider bars and finally gliding sliders of the head. In this work, we concentrate on reading serial numbers of slider bar and its attached fixture prior to a bar-lapping process. Since the serial numbers on a bar are in the scale of 18x30 microns and their reflectance nature is rather poor, special optics and lighting components are required. As nature of high optical magnification with limited depth of focus (DOF), conventional image acquisition system can not cover the bar attachment position variation due to wide tolerance of the slider bar fixture. We proposed a serial number reading system using autofocus module to cope with it. Liquid lens and image processing algorithms are introduced to evaluate and adjust the focus automatically. Since our system has to be augmented to an existing machine, footprint is also another main concern. We therefore designed a special rig so the system meets all the fore-mentioned requirements. The system was tested in the production line and had 98% and 90% accuracy for serial number and barcode decoding, respectively. The main cause of barcode reading inaccuracy was from many cycles of the fixture reuse and cleaning in the production line.
References
K. Sansomboon, B. Sangprtngam and R. Oonsivilai, “Increasing Accuracy by Number of Sectional Images used in Processing the Aggregate Gradation in Asphalt Concrete,” Ladkrabang Engineering Journal, vol. 34, no. 3, pp70–77, 2017.
K. Boonma and S. Klongboonjit, “The Application of Employing the Discriminant Analysis Technique to Forecast the Inspection Marking on the Integrated Circuit Product,” Ladkrabang Engineering Journal, vol. 39, no. 1, 2022.
J. Y. Chang, K. Fawzi, R. Moates and E. Rothenberg, “Image processing of novel vision-assisted hard disk drive flex cable-actuator manufacturing.” Microsystem Technologies, vol. 15, pp. 1637–1643, 2009, doi: 10.1007/s00542-009-0864-8.
W. Withayachumnankul, P. Kunakornvong, C. Asavathongkul and P. Sooraksa, “Rapid detection of hairline cracks on the surface of piezoelectric ceramics.” The International Journal of Advanced Manufacturing Technology, vol. 64, pp 1275–1283, 2013, doi: 10.1007/s00170-012-4085-4.
C. Mak, N. Afzulpurkar, M. Dailey and P. Saram, “A bayesian approach to automated optical inspection for solder jet ball joint defects in the head gimbal assembly process,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4, pp. 1155–1162, 2014, doi:10.1109/TASE.2014.2305654
S. Yammen and P. Muneesawang, “An advanced vision system for the automatic inspection of corrosions on pole tips in hard disk drives,” IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 4, no. 9, pp. 1523–1533, 2014, doi: 10.1109/TCPMT.2014.2334691.
B. Sae-ang and P. Mittrapiyanurak, P. Kaewtrakulpong, W. Kumwilaisak and S. Laohavichien, “Autofocus Machine Vision System for Reading Serial Numbers of Hard Disk Drive Slider Bar,” in 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC- CSCC), Phuket, Thailand, Jul. 05–08, 2022, pp. 345-348.
R. Subramanian, E. Spalding and N. Ferrier, “A high throughput robot system for machine vision based plant phenotype studies,” Machine Vision and Applications, vol. 24, pp. 619–636, 2013, doi: 10.1007/s00138-012-0434-4.
H. Bilen, M. Hocaoglu, M. Unel and A. Sabanovic, “Developing robust vision modules for microsystems applications,” Machine Vision and Applications, vol. 23, pp. 25–42, 2012, doi: 10.1007/s00138-010-0267-y
A. A. Bell, T. Würflinger, S.-O. Ropers, A. Böcking and T. Aach, “Towards fully automatic acquisition of multimodal cytopathological microscopy images with auto- focus and scene matching,” Methods of Information in Medicine, vol. 46, no. 3, pp. 314–323, 2007, doi: 10.1160/ME9049
G. P. Allen, R. Hodgson, S. Marsland and J. Flenley, “Machine vision for automated optical recognition and classification of pollen grains or other singulated microscopic objects.” in 15th International Conference on Mechatronics and Machine Vision in Practice, Auckland, New Zealand, Dec. 2–4, 2008, pp. 221–226.
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