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Conference Paper
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Year 2023, Volume: 22, 74 - 80, 01.09.2023

Oguz Alper Isen Emin Cantez Serkan Aydın

https://doi.org/10.55549/epstem.1337617

Abstract

References

  • Ahmed, S., Choudhury, I. A., Rahman, M. A., & Kamruzzaman, M. (2017). An automated production process for metal forming industry. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 209-212). IEEE.
  • Gu, Y., Cao, Y., Liu, H., & Shen, W. (2018). Research on the intelligent control system of the cushion in sheet metal forming. Journal of Intelligent Manufacturing, 29(4), 839-848.
  • Huang, J., He, J., & Zhou, H. (2016). Smart manufacturing for industry 4.0: a review. IEEE/ASME Transactions on Mechatronics, 22(2), 589-597.
  • Kalpakjian, S., & Schmid, S. R. (2014). Manufacturing processes for engineering materials (5th ed.). Pearson.
  • Liu, H., & Shen, W. (2016). Intelligent cushion control for sheet metal forming process based on fuzzy algorithm. The International Journal of Advanced Manufacturing Technology, 83(9-12), 2111-2122.
  • Park, H. K., Lee, K. M., Kim, J. K., & Kim, C. J. (2017). Development of an automatic cushion pin position control system for a hydraulic press using vision sensors. The International Journal of Advanced Manufacturing Technology, 92(1-4), 479-486.
  • Roopali, S., & Bharti, S. K. (2019). Industrial automation using machine vision: a review. SN Applied Sciences, 1(8), 776.
  • Saleh, A. A., & Abouelatta, O. B. (2017). Real-time control of metal forming processes using fuzzy logic. Journal of Manufacturing Processes, 26, 361-370.
  • Santochi, M., Dini, G., & Emanuele, R. (2019). An Industry 4.0-based approach for the optimization of a sheet metal forming process. Journal of Intelligent Manufacturing, 30(1), 229-237.
  • Sharma, S., & Saxena, S. K. (2018). A review of deep learning techniques for image classification. Journal of Computational and Theoretical Nanoscience, 15(5), 2037-2046.

Cushion Pin Control System with Using Image Processing

Year 2023, Volume: 22, 74 - 80, 01.09.2023

Oguz Alper Isen Emin Cantez Serkan Aydın

https://doi.org/10.55549/epstem.1337617

Abstract

Gas cylinders and cushion pins can be used in presses, which are the most used machines in metal forming.In the placement of the cushion pins on the press table, the operators find the right holes with the help of a map and place the cushion pins into the holes.When the cushion pins are placed in the wrong holes, it can cause permanent damage to the press, mold or metal sheets.In order to prevent these errors, although there are systems to guide the operators visually, there are no systems to control each hole according to each map. In the study, a virtual orientation was placed on the real image according to the map information using AR technology. Finally, each hole was controlled using image processing and deep learning methods using motion sensors on the camera, lidar and tablet.As a result, an inexpensive and effective result was produced using only a tablet with a camera and lidar.

Keywords

Image processing Deep learning Metal shaping Cushion pin Control system

References

  • Ahmed, S., Choudhury, I. A., Rahman, M. A., & Kamruzzaman, M. (2017). An automated production process for metal forming industry. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 209-212). IEEE.
  • Gu, Y., Cao, Y., Liu, H., & Shen, W. (2018). Research on the intelligent control system of the cushion in sheet metal forming. Journal of Intelligent Manufacturing, 29(4), 839-848.
  • Huang, J., He, J., & Zhou, H. (2016). Smart manufacturing for industry 4.0: a review. IEEE/ASME Transactions on Mechatronics, 22(2), 589-597.
  • Kalpakjian, S., & Schmid, S. R. (2014). Manufacturing processes for engineering materials (5th ed.). Pearson.
  • Liu, H., & Shen, W. (2016). Intelligent cushion control for sheet metal forming process based on fuzzy algorithm. The International Journal of Advanced Manufacturing Technology, 83(9-12), 2111-2122.
  • Park, H. K., Lee, K. M., Kim, J. K., & Kim, C. J. (2017). Development of an automatic cushion pin position control system for a hydraulic press using vision sensors. The International Journal of Advanced Manufacturing Technology, 92(1-4), 479-486.
  • Roopali, S., & Bharti, S. K. (2019). Industrial automation using machine vision: a review. SN Applied Sciences, 1(8), 776.
  • Saleh, A. A., & Abouelatta, O. B. (2017). Real-time control of metal forming processes using fuzzy logic. Journal of Manufacturing Processes, 26, 361-370.
  • Santochi, M., Dini, G., & Emanuele, R. (2019). An Industry 4.0-based approach for the optimization of a sheet metal forming process. Journal of Intelligent Manufacturing, 30(1), 229-237.
  • Sharma, S., & Saxena, S. K. (2018). A review of deep learning techniques for image classification. Journal of Computational and Theoretical Nanoscience, 15(5), 2037-2046.
There are 10 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Oguz Alper Isen

Emin Cantez

Serkan Aydın

Early Pub Date August 3, 2023
Publication Date September 1, 2023
Published in Issue Year 2023 Volume: 22

Cite

APA Isen, O. A., Cantez, E., & Aydın, S. (2023). Cushion Pin Control System with Using Image Processing. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 22, 74-80. https://doi.org/10.55549/epstem.1337617
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