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논문 기본 정보

자료유형
학술저널
저자정보
저널정보
대한용접·접합학회 International Journal of Korean Welding Society International Journal of Korean Welding Society Vol.1 No.1
발행연도
2001.5
수록면
17 - 22 (6page)

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초록· 키워드

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Because the quantitative relationships between welding parameters and welding result are not yet known, optimal values of welding parameters for CO₂ robotic are welding is a difficult task. Using the various, artificial data processing methods may solve this difficulty. This research aims to develop an expert system for CO₂ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis ,md fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current, voltage, speed, weaving width. and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule, as learning algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of 겨les. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic CO₂ fillet welding with 1.2, 1.4. and 1.6mm diameter the solid wires or flux-cored wires.

목차

Abstract

1.Introduction

2.Relationship in between the welding parameters and the welding result

3.Fuzzy system

4.Prediction of bead shape using the neural network

5.Optimization using the knowledge base system

6.Structure of the expert system

7.Conclusion

References

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UCI(KEPA) : I410-ECN-0101-2009-581-014263000