To estimate weld quality of the resistance spot-welding, the acoustic emission features are investigated from the total acoustic emission signal at the single-spot weld. Typically, the resistance spot welding process consists of several stages: set-down of the electrodes, squeeze, current flow, forging, hold time, and lift-off. Various types of acoustic emission response corresponding to each stage can be separately analyzed by using back-propagation neural network classifier and wavelet transform technique. The presented machine learning results provide a validation for using back-propagation neural network and wavelet transform technique as a valuable insights into the resistance spot-welding process. Especially, a wavelet transform technique is demonstrated and the plots are very powerful in the recognition of the acoustic emission features

Abstract
 1. 서론
 2. 관련 이론
  2.1 웨이블릿 변환
  2.2 역전파 신경망
 3. 실험 및 방법
 4. 실험결과 및 신호해석
 5. 결론
 후기
 References

• 우창기(인천대학교 기계시스템공학부) | Chang-Ki Woo
• 이장규(인천대학교 기계시스템공학부) | Zhang-Kyu Rhee Corresponding Author