The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection

The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection

Recently the industries provide visual inspection processes in the plants for keeping and guaranteeing product quality. Many visual inspection processes are normally operated by the manual visual inspection. The results of the manual visual inspection are often unstable because the results are depen...

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Main Author: Teng, Guat Theng
Format: Thesis
Language:English
English
Published: 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utem.edu.my/id/eprint/25582/1/The%20Capability%20Study%20On%20Automated%20Visual%20Inspection%20%28AVI%29%20Machine%20For%20FPCB%20Inspection.pdf
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record_format uketd_dc
institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Zamri, Ruzaidi
topic T Technology (General)
T Technology (General)
spellingShingle T Technology (General)
T Technology (General)
Teng, Guat Theng
The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
description Recently the industries provide visual inspection processes in the plants for keeping and guaranteeing product quality. Many visual inspection processes are normally operated by the manual visual inspection. The results of the manual visual inspection are often unstable because the results are depended on the inspection worker skill. Currently the automated visual inspection (A VI) technologies are getting more important to stably keep and guarantee product quality. MFS Technology (M) Sdn. Bhd. are owned a semiauto A VI machine but it not fully utilize. Therefore, this study will study on the machine capability to access the machine capability on the Flexible Printed Circuit Board (FPCB) inspection at final inspection stage. In MFS Technology, the finished goods products is required to perform 100% human visual inspection before shipment to prevent defect escapee and guarantee the product quality. However the human inspection will bring a lot of uncertainty due to human factor such as fatigue, lack of awareness and etc. Therefore, A VI machine is required to automatically detect the defective mode for inspector to perform final judgment based on acceptance criteria. This study will perform correlation study and MSA analysis to access the AVl machine capability by using Top 3 defect that frequently detected during visual inspectlila~hich is Embedded Foreign Material (EFM), Expose Copper on Gold (ECG), and coverlay damage (CD). Each defect mode are collected 10 samples and tested by 3 appraiser for 3 trail to perform repeatability and reproducibility (GR&R) study. The result of the study indicted that the capability study of the A VI machine is 100% effectiveness with Kappa value is 1.0. In short, the capability study on A VI machine is capable detect the FPCB for top 3 defect without any missed judgment.
format Thesis
qualification_name Master of Philosophy (M.Phil.)
qualification_level Master's degree
author Teng, Guat Theng
author_facet Teng, Guat Theng
author_sort Teng, Guat Theng
title The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
title_short The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
title_full The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
title_fullStr The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
title_full_unstemmed The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection
title_sort capability study on automated visual inspection (avi) machine for fpcb inspection
granting_institution https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119124
granting_department Faculty of Manufacturing Engineering
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/25582/1/The%20Capability%20Study%20On%20Automated%20Visual%20Inspection%20%28AVI%29%20Machine%20For%20FPCB%20Inspection.pdf
http://eprints.utem.edu.my/id/eprint/25582/2/The%20Capability%20Study%20On%20Automated%20Visual%20Inspection%20%28AVI%29%20Machine%20For%20FPCB%20Inspection.pdf
_version_ 1747834143782207488
spelling my-utem-ep.255822022-01-06T14:30:29Z The Capability Study On Automated Visual Inspection (AVI) Machine For FPCB Inspection 2020 Teng, Guat Theng T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Recently the industries provide visual inspection processes in the plants for keeping and guaranteeing product quality. Many visual inspection processes are normally operated by the manual visual inspection. The results of the manual visual inspection are often unstable because the results are depended on the inspection worker skill. Currently the automated visual inspection (A VI) technologies are getting more important to stably keep and guarantee product quality. MFS Technology (M) Sdn. Bhd. are owned a semiauto A VI machine but it not fully utilize. Therefore, this study will study on the machine capability to access the machine capability on the Flexible Printed Circuit Board (FPCB) inspection at final inspection stage. In MFS Technology, the finished goods products is required to perform 100% human visual inspection before shipment to prevent defect escapee and guarantee the product quality. However the human inspection will bring a lot of uncertainty due to human factor such as fatigue, lack of awareness and etc. Therefore, A VI machine is required to automatically detect the defective mode for inspector to perform final judgment based on acceptance criteria. This study will perform correlation study and MSA analysis to access the AVl machine capability by using Top 3 defect that frequently detected during visual inspectlila~hich is Embedded Foreign Material (EFM), Expose Copper on Gold (ECG), and coverlay damage (CD). Each defect mode are collected 10 samples and tested by 3 appraiser for 3 trail to perform repeatability and reproducibility (GR&R) study. The result of the study indicted that the capability study of the A VI machine is 100% effectiveness with Kappa value is 1.0. In short, the capability study on A VI machine is capable detect the FPCB for top 3 defect without any missed judgment. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25582/ http://eprints.utem.edu.my/id/eprint/25582/1/The%20Capability%20Study%20On%20Automated%20Visual%20Inspection%20%28AVI%29%20Machine%20For%20FPCB%20Inspection.pdf text en 2025-08-19 validuser http://eprints.utem.edu.my/id/eprint/25582/2/The%20Capability%20Study%20On%20Automated%20Visual%20Inspection%20%28AVI%29%20Machine%20For%20FPCB%20Inspection.pdf text en 2025-08-19 validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119124 mphil masters https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119124 Faculty of Manufacturing Engineering Zamri, Ruzaidi 1. Al-amri, S. S. (2010) Image segmentation by using edge detection , 02(03), pp. 804-807. Analysis, I. and ScienceSoft, A. (2019) Automated Visual Inspection Software, ScienceSoft. Available at: https://www.scnsoft.com/services/image analysis/automatedvisual-inspection (Accessed: 7 April 2019). 2. Azad, M. M., Hasan, M. and Naseer, K. M. (2017) Color Image Processing in Digital Image’, (November). 3. Bukhari, S. U., Bondrea, I. and Brad, R. (2017) ‘Automated PCB inspection system’, TEM Journal, 6(2), pp. 380-390. doi: 10.18421/TEM62-25. 4. Cepova, L. et al. (2018) ‘Measurement System Analyses - Gauge Repeatability and Reproducibility Methods’, Measurement Science Review, 18(1), pp. 20-27. doi: 10.1515/msr-2018-0004. 5. Chaudhary, V., Dave, I. . and Upla, K. . (2017) ‘Automatic Visual Inspection of Printed Circuit Board for Defect Detection and Classification’, pp. 732-737. 6. Chitradevi, B. and Srimathi, P. (2014) ‘An Overview on Image Processing Techniques’, pp. 6466-6472. 7. Dharampal and Mutneja, V. (2015) ‘Methods of Image Edge Detection : A Review’, 4(2). 8. Diab, S. L., Hussein, M. and Ahmed, R. (2011) ‘Designing a Visual Inspection System for Quality Characteristics Dimensions’, 1(1), pp. 12-23. 9. Fung, A. and Yin, A. (2012) ‘Analysis on Combination of AOI and AVI machines’. Gonzalez, R. C. (2017) Digital Image Processing. 4th editio. Pearson Education (US). 10. Huang, S. and Pan, Y. (2015) Automated visual inspection in the semiconductor industry : A survey , Computers in Industry. Elsevier B.V., 66, pp. 1-10. doi: 10.1016/j.compind.2014.10.006. 11. Kansal, J. and Singhal, S. (2018) Automation of Visual Inspection Using Image Processing Industrial Engineering Journal, 10(10), pp. 37-47. doi: 10.26488/IEJ.6.10.5. 12. Ke-ming, Z. I., Jiangsu, L. and Hua-bing, W. (2012) ‘Automated Visual Inspection and its Application on Automated Inspection’, (Mems), pp. 637-638. 13. Mariappan, M. et al. (2016) ‘Automated Visual Inspection : Position Identification of Object for Industrial Robot Application based on Color and Shape’, (January), pp. 9-17. 14. MFS Technology (2016). Available at: http://mfstech.com.sg/ (Accessed: 7 April 2019). 15. Mogharrebi, M. et al. (2016) ‘Vision based inspection system for missing footprint detection on printed circuit boards’, Journal of Theoretical and Applied Information Technology, 84(1), pp. 10-18. doi: 10.528l /zenodo.572619. 16. Newman; T. and Jain; A. (1995) ‘A Survey of Automated Visual Inspection.pdf , 61, pp. 231-262. 17. Raihan, F. and Ce, W. (2017) ‘PCB Defect Detection USING OPENCV with Image Subtraction Method’, (November), pp. 204-209. 18. Sablatnig, R. (1997) ‘A Highly Adaptable Concept for Visual Inspection Acknowledgments’. 19. Sarikan, S. S., Ozbayoglu, A. M. and Zilci, O. (2017) ‘Automated Vehicle Classification with Image Processing and Computational Intelligence’, in Procedia Computer Science. Elsevier B.V., pp. 515-522. doi: 10.1016/j.procs.2017.09.022. 20. Simion, C. (2016) ‘Evaluation of an attributive measurement system in the automotive industry’, in IOP Conference Series: Materials Science and Engineering, doi:10.1088/1757-899X/145/5/052005. 21. Sivaji, A. (2010) Measurement system analysis , in Chrysler Group LLC, Ford Motor Company, G. M. C. (ed.) Measurement System Analysis. Fourth Edi. AIAG, pp. 393-396. doi: 10.1109/DELTA.2006.62. 22. Spcforexcel.com (2010) Attribute Gage R&R Studies: Comparing Appraisers, BPI Consulting. Available at: https://www.spcforexcel.com/knowledge/measurement-systemsanalysis/attribute-gage-rr-comparing-appraisers (Accessed: 27 May 2019). 23. Stancic, I. V. O., Supuk, T. and Cecic, M. (2014) Computer Vision System for Human Anthropometric Parameters Estimation’, (June). 24. Taha, E. M., Emary, E. and Moustafa, K. (2014) ‘Automatic Optical Inspection for PCB Manufacturing : a Survey’, 5(7), pp. 1095-1102. 25. Zhou, Q. et al. (2019) ‘An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods’ , doi: 10.3390/sl9030644.

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