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A Case Study on Quality Improvement of Electric Vehicle Hairpin Winding Motor Using Deep Learning AI Solution

딥러닝 AI 솔루션을 활용한 전기자동차 헤어핀 권선 모터의 용접 품질향상에 관한 사례연구

  • Lee, Seungzoon (Department of IT Policy and Management, Graduate School of Soongsil University) ;
  • Sim, Jinsup (Department of IT Policy and Management, Graduate School of Soongsil University) ;
  • Choi, Jeongil (College of Business Administration, Soongsil University)
  • 이승준 ( 숭실대학교 대학원 IT정책경영학과) ;
  • 심진섭 ( 숭실대학교 대학원 IT정책경영학과) ;
  • 최정일 (숭실대학교 경영학부)
  • Received : 2023.03.24
  • Accepted : 2023.04.07
  • Published : 2023.06.30

Abstract

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

Keywords

References

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