Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Laser Process Parameters using Bead Image Data

비드 이미지 데이터를 활용한 레이저 공정변수 예측

  • Jeon, Ye-Rang (Graduate School of Mechanical Engineering, Keimyung Univ.) ;
  • Choi, Hae-Woon (Dept, of Mechanical Engineering Keimyung Univ.)
  • 전예랑 (계명대학교 대학원 기계공학과) ;
  • 최해운 (계명대학교 기계공학전공)
  • Received : 2022.04.22
  • Accepted : 2022.05.18
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study reports experiments were conducted to determine the quality of weld beads of different materials, Al and Cu. Among the lasers used to make battery cells for electric vehicles, non-destructive testing was performed using deep learning to determine the quality of beads welded with the ARM laser. Deep learning was performed using AlexNet algorithm with a convolutional neural network structure. The results of quality identification were divided into good and bad, and the result value was derived that all the results were in agreement with 94% or more. Overall, the best welding quality was obtained in the experiment for the fixed ring beam output/variable center beam output, in the case of the fixed beam (ring beam) 500W and variable beam (center beam) 1,050W; weld bead failure was seldom observed. The tensile force test to confirm the reliability of welding reported an average tensile force of 2.5kgf/mm or more in all sections.

Keywords

References

  1. Chung, S. M., "Electric Vehicle Driving System Technology," The Korean Society Automotive Engineers, Vol. 32, No. 8, pp. 28-32, 2010.
  2. Jung, D. H. and Jung, J. P., "Aluminum alloys and their joining methods," Journal of the Microelectronics and Packaging Society, Vol. 25, No. 2, pp. 9-17, 2018. https://doi.org/10.6117/KMEPS.2018.25.2.009
  3. Lara, A. and Milton, S., "Cracking susceptibility of aluminum alloys during laser welding," Materials Research, Vol. 6, No. 2, pp. 273-278, 2003. https://doi.org/10.1590/S1516-14392003000200024
  4. Kang, E. J. and Min, K. T., "Temperature Distribution Analysis of Welding Parts in Ultrasonic Welding by Using FEM," Journal of the Korean Society of Manufacturing Technology Engineers, Vol. 25, No. 2, pp. 105-111, 2016. https://doi.org/10.7735/ksmte.2016.25.2.105
  5. Song, S. W., Kim, N. K. and Kang, C. Y., "Dissimilar Friction Stir Welding of Aluminum Alloys," Journal of KWJS, Vol. 27, No. 5, October, 2009.
  6. Park, E. K., Lee, K. R., Lee, H. J. and Yoo, Y., "Study on Fiber Laser Welding Characteristics of Copper for Secondary Battery Material," Korean Society of Laser Processing, Vol. 17, No. 3, pp. 1-9, 2014.
  7. Oliver, B., Nicolai, S., Matthias, B. and Tim, H., "Reduction of spatter and pore in laser welding of copper hairpins using two superimposed laser beams," Korean Society of Laser Processing, Vol. 23., No. 12, pp. 14-20, 2020.
  8. Kim, T. W. and Choi, H. W., "Study on Laser Welding of Al-Cu Dissimilar Material by Green Laser and Weld Quality Evaluation by Deep Learning," Journal of Welding and Joining, Vol. 39, No. 1, pp. 67-73, 2021. https://doi.org/10.5781/JWJ.2021.39.1.8