A Study on the Welding Gap Detecting Using Pattern Classification by ART2 and Fuzzy Membership Filter

• 영문명: A Study on the Welding Gap Detecting Using Pattern Classification by ART2 and Fuzzy Membership Filter
• 발행기관: 한국해양대학교 해사산업연구소
• 저자명: Tae-Yeong Kim Sang-Bae Lee
• 간행물 정보: 『해사산업연구소논문집』제8집, 227~241쪽, 전체 15쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 1998.12.01

영문 초록

This study introduces to the fuzzy membership filter to cancel a high frequency noise of welding current. And ART2 which has the competitive learning network classifies the signal patterns for the filtered welding signal. A welding current possesses a specific pattern according to the existence or the size of a welding gap. These specific patterns result in different classification in comparison with an occasion for no welding gap. The patterns in each case of 1mm, 2mm, 3mm and no welding gap are identified by the artificial neural network. These procedure is an off-line execution. In on-line execution, the identification model of neural network for the classified pattern is located on ahead of the welding plant. And when the welding current patterns pass through the neural network in the direction of feedforward, it is possible to recognize the existence or the size of a welding gap.

목차

Abstract

1. Introduction

2. Fuzzy Membership Filter

3. Simulation

4. Conclusion

References