AI Based Quality Analysis of Industrial Products

Year : 2024 | Volume :14 | Issue : 01 | Page : 39-45
By

Prasanna Khandarkar,

Shyam Ingle,

Adwait Kulkarni,

Sapana S. Kamble,

  1. Student Department of Electronics & Telecommunication, Sinhgad College of Engineering, Savitribai Phule University, Pune Maharashtra India
  2. Student Department of Electronics & Telecommunication, Sinhgad College of Engineering, Savitribai Phule University, Pune Maharashtra India
  3. Student Department of Electronics & Telecommunication, Sinhgad College of Engineering, Savitribai Phule University, Pune Maharashtra India
  4. Professor Department of Electronics & Telecommunication, Sinhgad College of Engineering, Savitribai Phule University, Pune Maharashtra India

Abstract

The project introduces a new method for sampling data from a group, addressing limitations in traditional approaches. It highlights inefficiencies in inspecting only a few items from a batch, potentially leading to the rejection of entire lots due to isolated errors. To counter these challenges, a system is being designed to automatically inspect every gear in a batch. This system incorporates a conveyor belt for gear movement and a camera for analyzing gear parameters. Parameters are compared against those stored in a backend database, with gears meeting the specified criteria directed to the accepted lot and those failing diverted to the rejected lot using a shooting gun mechanism. This automated approach aims to enhance efficiency and accuracy in quality control, eliminating manual inspection and reducing the risk of rejecting entire lots due to isolated errors.

Keywords: Gear, MATLAB, Image processing, Manufacturing industries, Power transmission

[This article belongs to Journal of Production Research & Management(joprm)]

How to cite this article: Prasanna Khandarkar, Shyam Ingle, Adwait Kulkarni, Sapana S. Kamble. AI Based Quality Analysis of Industrial Products. Journal of Production Research & Management. 2024; 14(01):39-45.
How to cite this URL: Prasanna Khandarkar, Shyam Ingle, Adwait Kulkarni, Sapana S. Kamble. AI Based Quality Analysis of Industrial Products. Journal of Production Research & Management. 2024; 14(01):39-45. Available from: https://journals.stmjournals.com/joprm/article=2024/view=147563

Browse Figures

References

  1. Haque Nawaz, Himat Ali, “Gear Measurement Using Image Processing in Matlab”, ‘International Journal of Innovative Technology and Exploring Engineering (IJITEE)’, 2014, Volume-3, pp 43-47.
  2. Cheng Pengfei and Feng Changyong Henan, “Characteristic Value Extraction of Gear Defect Based on Image Processing”, ‘Journal of Multimedia’, 2013, Volume- 8, pp 198-205.
  3. Alkhadafe, A. Al-Habaibeh, A. Lotfi, Condition monitoring of helical gears using automated selection of features and sensors, Measurement (2016)
  4. Hazrat Ali, Syuhei Kurokawa,Kensuke Uesugi ‘ Camera Based Precision Measurement In Improving Measurement Accuracy’ Measurement 49(2014) 138-147.
  5. Zhang Jing, “The Research on the Application to Digital Image Technology in Hypoid Gear Contact Zone Detection”, ‘Journal of Computers’, 2011, Volume-. 6, PP 1519-1525
  6. García-Hernández, C., Gella-Marín, R., Huertas-Talón, J. L., & Berges-Muro, L. (2016). Algorithm for measuring gears implemented with general-purpose spreadsheet software. Measurement, 85, 1-12.
  7. Wan, F., Luo, H., & Liu, J. (2019, August). Online high-precision vision measurement method for large-size gear parameters. In Proceedings of the 2019 2nd International Conference on Robot Systems and Applications (pp. 20-24).
  8. Wijaya, T., Caesarendra, W., Tjahjowidodo, T., Pappachan, B. K., Wee, A., & Roslan, M. I. (2018). A review on sensors for real-time monitoring and control systems on machining and surface finishing processes.
  9. Khan, A. W., & Chen, W. (2009, February). Calibration of CNC milling machine by direct method. In 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications (Vol. 7160, pp. 266-275). SPIE.
  10. Moroni, G., & Petrò, S. (2013). Automatic cutting-edge detection for a cylindrical mill. High value manufacturing: advanced research in virtual and rapid prototyping. CRC Press, Boca Raton, 457-461.

Regular Issue Subscription Original Research
Volume 14
Issue 01
Received April 23, 2024
Accepted May 3, 2024
Published May 24, 2024