Research Article
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Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

Year 2021, Volume: 4 Issue: 2, 16 - 31, 31.12.2021
https://doi.org/10.38061/idunas.850545

Abstract

Emerging in the past few decades, Industry 4.0 has wide effects over production lines with an increasing number of novel applications. These applications implement more than one of the tools of Industry 4.0. These tools include but are not limited to internet of things (IoT), big data, cloud computing, artificial intelligence, augmented reality, virtual reality, machine to machine communication (M2M), smart robot applications, etc. The aim of these efforts is mainly to acquire smarter manufacturing systems. With spreading Industry 4.0 methodologies, the role of sensors became more important to respond to new demands. One of the most important sensor types in this sense is the camera which now has wide variants in different forms. Applying machine vision algorithms via cameras grants optimization of many critical processes. In this perspective, the quality of both product and process should be handled as a key performance indicator that may be continuously enhanced for excellence. Machine vision algorithms may be adapted to check and manage quality in designated control points on the production lines. This study focuses on the control of the quality of rotary switches that are widely used in household appliances like ovens and washing machines. Rotary switches are critical components of an appliance since they direct the flow of electricity within the product. A failure in the functionality of this component directly causes the failure of the main product. Hence, the quality rate of rotary switches should be calculated in defective parts per million (dppm) units. An intense quality control procedure is required to achieve low dppm rates during production. As a real-life application, a camera system is integrated into the rotary switch production line on a selected point. Classification algorithms are developed on a cost-effective platform to perform visual quality checks of the rotary switches and qualify as “Ok” or “Defective”. The selected point ensures a high percent check of quality criteria while enabling repair of the defective parts with minor interventions. The aim of this control is to identify a defective rotary switch as soon as possible since most of the defects are irreversible once the rotary switch is totally produced or even some processes are completed. In this case, the entire product should be set apart for scrap.
Another originality of our study is applying both Process Failure Mode and Effect Analysis (PFMEA) and Design Failure Mode and Effect Analysis (DFMEA) together. There is almost no referenced study in the literature.
Benchmark comparisons are conducted upon completing the integration of the new system to the production line. As a result, enhancements in the quality, cost, and production speed parameters are achieved with a cost-effective smart system. Additional capabilities are added to the system, namely data analyzing online data feeding.

Thanks

The study is conducted in and supported by AN-EL Anahtar ve Elektrikli Ev Aletleri Sanayi A.S.. We thank the Top Management and R&D Center Team Members for their contributions.

References

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  • Wang, C. H., Guo, R. S., Chiang, M. H., & Wong, J. Y. (2008). Decision tree based control chart pattern recognition. International Journal of Production Research, 46(17), 4889-4901.
  • Matsko, I. I., Logunova, O. S., Pavlov, V. V., & Matsko, O. S. (2012). Adaptive fuzzy decision tree with dynamic structure for automatic process control system o of continuous-cast billet production. IOSR Journal of Engineering, 2(8), 53-55.
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  • Agarwala, A. S. (1990, January). Shortcomings in mil-std-1629A guidelines for criticality analysis. In Annual Proceedings on Reliability and Maintainability Symposium (pp. 494-496). IEEE.
  • Office of manned space flight, Apollo program, Apollo Reliability and Quality Assurance Office. (1966). Procedure for Failure Mode, Effects and Criticality Analysis (FMECA).
  • Maddalena, S., Darmon, A., & Diels, R. (2005). Automotive CMOS image sensors. In Advanced Microsystems for Automotive Applications 2005 (pp. 401-412). Springer, Berlin, Heidelberg.
  • Klochkov, Y., Its, A., & Vasilieva, I. (2016). Development of FMEA method with the purpose of quality assessment of can stock production. In Key Engineering Materials (Vol. 684, pp. 473-476). Trans Tech Publications Ltd.
  • Sharma, K. D., & Srivastava, S. (2018). Failure mode and effect analysis (FMEA) implementation: a literature review. J. Adv. Res. Aeronaut. Space Sci, 5, 1-17.
  • Spreafico, C., Russo, D., & Rizzi, C. (2017). A state-of-the-art review of FMEA/FMECA including patents. Computer Science Review, 25, 19-28.
  • Huang, J., You, J. X., Liu, H. C., & Song, M. S. (2020). Failure mode and effect analysis improvement: A systematic literature review and future research agenda. Reliability Engineering & System Safety, 106885.
  • Ng, W. C., Teh, S. Y., Low, H. C., & Teoh, P. C. (2017, September). The integration of FMEA with other problem solving tools: A review of enhancement opportunities. In J Phys Conf Ser (Vol. 890, p. 012139).
  • AIAG-VDA FMEA Handbook 4. Edition (2019)
  • Puvanasvaran, A. P., Jamibollah, N., Norazlin, N., & Adibah, R. (2014). Poka-Yoke Integration into process FMEA. Australian Journal of Basic and Applied Sciences, 8(7), 66-73.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5), 360-363.
  • Chauhan, V., & Surgenor, B. (2015). A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manufacturing, 1, 416-428.
  • Kunakornvong, P., & Sooraksa, P. (2016). Machine vision for defect detection on the air bearing surface. In 2016 International Symposium on Computer, Consumer and Control (IS3C) (pp. 37-40). IEEE.
  • Shen, H., Li, S., Gu, D., & Chang, H. (2012). Bearing defect inspection based on machine vision. Measurement, 45(4), 719-733.
  • Edinbarough, I., Balderas, R., & Bose, S. (2005). A vision and robot based on-line inspection monitoring system for electronic manufacturing. Computers in Industry, 56(8-9), 986-996.
Year 2021, Volume: 4 Issue: 2, 16 - 31, 31.12.2021
https://doi.org/10.38061/idunas.850545

Abstract

References

  • Hernández, B., Jiménez, J., & Martín, M. J. (2010). Customer behavior in electronic commerce: The moderating effect of e-purchasing experience. Journal of business research, 63(9-10), 964-971.
  • Gunasekaran, A. (1999). Agile manufacturing: a framework for research and development. International journal of production economics, 62(1-2), 87-105.
  • Louw, L., & Droomer, M. (2019). Development of a low cost machine vision based quality control system for a learning factory. Procedia Manufacturing, 31, 264-269.
  • Würschinger, H., Mühlbauer, M., Winter, M., Engelbrecht, M., & Hanenkamp, N. (2020). Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware. Procedia CIRP, 90, 611-616.
  • Ardhy, F., & Hariadi, F. I. (2016, November). Development of SBC based machine-vision system for PCB board assembly automatic optical inspection. In 2016 International Symposium on Electronics and Smart Devices (ISESD) (pp. 386-393). IEEE.
  • Gong, Y., Lin, Z., Wang, J., & Gong, N. (2018). Bringing machine intelligence to welding visual inspection: development of low-cost portable embedded device for welding quality control. Electronic Imaging, 2018(9), 279-1.
  • Korodi, A., Anitei, D., Boitor, A., & Silea, I. (2020). Image-processing-based low-cost fault detection solution for end-of-line ECUs in automotive manufacturing. Sensors, 20(12), 3520.
  • Adamo, F., Attivissimo, F., Di Nisio, A., & Savino, M. (2009). A low-cost inspection system for online defects assessment in satin glass. Measurement, 42(9), 1304-1311.
  • Frustaci, F., Perri, S., Cocorullo, G., & Corsonello, P. (2020). An embedded machine vision system for an in-line quality check of assembly processes. Procedia Manufacturing, 42, 211-218.
  • Parakontan, T., & Sawangsri, W. (2019, June). Development of the Machine Vision System for Automated Inspection of Printed Circuit Board Assembl. In 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS) (pp. 244-248). IEEE.
  • Di Leo, G., Liguori, C., Pietrosanto, A., & Sommella, P. (2017). A vision system for the online quality monitoring of industrial manufacturing. Optics and Lasers in Engineering, 89, 162-168.
  • Moru, D. K., & Borro, D. (2020). A machine vision algorithm for quality control inspection of gears. The International Journal of Advanced Manufacturing Technology, 106(1), 105-123.
  • Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660-674.
  • Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
  • Wang, C. H., Guo, R. S., Chiang, M. H., & Wong, J. Y. (2008). Decision tree based control chart pattern recognition. International Journal of Production Research, 46(17), 4889-4901.
  • Matsko, I. I., Logunova, O. S., Pavlov, V. V., & Matsko, O. S. (2012). Adaptive fuzzy decision tree with dynamic structure for automatic process control system o of continuous-cast billet production. IOSR Journal of Engineering, 2(8), 53-55.
  • Putri, N. K. S., Puika, K. S., Ibrahim, S., & Darmawan, L. (2018, September). Defect Classification Using Decision Tree. In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 281-285). IEEE.
  • Chen, J., Lian, Y., & Li, Y. (2020). Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm. Computers and Electronics in Agriculture, 175, 105591.
  • Zhou, Q., Chen, R., Huang, B., Liu, C., Yu, J., & Yu, X. (2019). An automatic surface defect inspection system for automobiles using machine vision methods. Sensors, 19(3), 644.
  • Lin, Y., Xiang, Y., Lin, Y., & Yu, J. (2019, September). Defect detection system for optical element surface based on machine vision. In 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE) (pp. 415-418). IEEE.
  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.
  • Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
  • Agarwala, A. S. (1990, January). Shortcomings in mil-std-1629A guidelines for criticality analysis. In Annual Proceedings on Reliability and Maintainability Symposium (pp. 494-496). IEEE.
  • Office of manned space flight, Apollo program, Apollo Reliability and Quality Assurance Office. (1966). Procedure for Failure Mode, Effects and Criticality Analysis (FMECA).
  • Maddalena, S., Darmon, A., & Diels, R. (2005). Automotive CMOS image sensors. In Advanced Microsystems for Automotive Applications 2005 (pp. 401-412). Springer, Berlin, Heidelberg.
  • Klochkov, Y., Its, A., & Vasilieva, I. (2016). Development of FMEA method with the purpose of quality assessment of can stock production. In Key Engineering Materials (Vol. 684, pp. 473-476). Trans Tech Publications Ltd.
  • Sharma, K. D., & Srivastava, S. (2018). Failure mode and effect analysis (FMEA) implementation: a literature review. J. Adv. Res. Aeronaut. Space Sci, 5, 1-17.
  • Spreafico, C., Russo, D., & Rizzi, C. (2017). A state-of-the-art review of FMEA/FMECA including patents. Computer Science Review, 25, 19-28.
  • Huang, J., You, J. X., Liu, H. C., & Song, M. S. (2020). Failure mode and effect analysis improvement: A systematic literature review and future research agenda. Reliability Engineering & System Safety, 106885.
  • Ng, W. C., Teh, S. Y., Low, H. C., & Teoh, P. C. (2017, September). The integration of FMEA with other problem solving tools: A review of enhancement opportunities. In J Phys Conf Ser (Vol. 890, p. 012139).
  • AIAG-VDA FMEA Handbook 4. Edition (2019)
  • Puvanasvaran, A. P., Jamibollah, N., Norazlin, N., & Adibah, R. (2014). Poka-Yoke Integration into process FMEA. Australian Journal of Basic and Applied Sciences, 8(7), 66-73.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5), 360-363.
  • Chauhan, V., & Surgenor, B. (2015). A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manufacturing, 1, 416-428.
  • Kunakornvong, P., & Sooraksa, P. (2016). Machine vision for defect detection on the air bearing surface. In 2016 International Symposium on Computer, Consumer and Control (IS3C) (pp. 37-40). IEEE.
  • Shen, H., Li, S., Gu, D., & Chang, H. (2012). Bearing defect inspection based on machine vision. Measurement, 45(4), 719-733.
  • Edinbarough, I., Balderas, R., & Bose, S. (2005). A vision and robot based on-line inspection monitoring system for electronic manufacturing. Computers in Industry, 56(8-9), 986-996.
There are 38 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

İsmet Karacan 0000-0003-4582-3337

İnanç Erdoğan This is me 0000-0001-7691-9337

Mustafa İğdil This is me 0000-0002-2130-2492

Ufuk Cebeci 0000-0003-4367-6206

Publication Date December 31, 2021
Acceptance Date March 4, 2021
Published in Issue Year 2021 Volume: 4 Issue: 2

Cite

APA Karacan, İ., Erdoğan, İ., İğdil, M., Cebeci, U. (2021). Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA. Natural and Applied Sciences Journal, 4(2), 16-31. https://doi.org/10.38061/idunas.850545