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

  • 제21권6호
  • /
  • Pages.8-14
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  • 2022
  • /
  • 1598-6721(pISSN)
  • /
  • 2288-0771(eISSN)
한국기계가공학회 (The Korean Society of Manufacturing Process Engineers)
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비드 이미지 데이터를 활용한 레이저 공정변수 예측

Prediction of Laser Process Parameters using Bead Image Data

  • 전예랑 (계명대학교 대학원 기계공학과) ;
  • 최해운 (계명대학교 기계공학전공)
  • Jeon, Ye-Rang (Graduate School of Mechanical Engineering, Keimyung Univ.) ;
  • Choi, Hae-Woon (Dept, of Mechanical Engineering Keimyung Univ.)
  • 투고 : 2022.04.22
  • 심사 : 2022.05.18
  • 발행 : 2022.06.30
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초록

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.

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참고문헌

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