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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.12
- 수록면
- 126 - 132 (7page)
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초록· 키워드
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The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations.
In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.
In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.
목차
Abstract
1. 서론
2. 용접 실험
3. 유전자 알고리즘
4. 추가 실험
5. 결론
후기
참고문헌
참고문헌 (8)
참고문헌 신청
1. [학술지(정기간행물)] - J. Raveendra / 1987 / Mathematical models to predict weld bead geometry for flux cored arc welding / Metal Construction 19 (2) : 31 ~ 35
2. [학술대회논문] - Chandel, R. S. / 1988 / Mathematical modelling of gasmetal arc weld features, Modeling and Control of Casting and Welding Processes Ⅳ / Proceedings of the Fourth International Conference on Modeling of Casting and Welding Processes, Palm Coast, Florida 17 (22) : 109 ~ 120
3. [학술지(정기간행물)] - I.S. Kim / 2001 / Selection of optimal Process Parameters for Butt Welding / The Korean Welding and Joining Society 2 : 3 ~ 6
4. [학술지(정기간행물)] - L.J. Yang / 1992 / The effects of process variables on the bead height of submerged-arc weld deposits / Candian Metallurgical Quarterly 31 (4) : 289 ~ 297
5. [단행본] - David E. Gldberg / 1989 / Genetic Algorithms in Search, Optimization and Machine Learning / Addison-Wesley
2. [학술대회논문] - Chandel, R. S. / 1988 / Mathematical modelling of gasmetal arc weld features, Modeling and Control of Casting and Welding Processes Ⅳ / Proceedings of the Fourth International Conference on Modeling of Casting and Welding Processes, Palm Coast, Florida 17 (22) : 109 ~ 120
3. [학술지(정기간행물)] - I.S. Kim / 2001 / Selection of optimal Process Parameters for Butt Welding / The Korean Welding and Joining Society 2 : 3 ~ 6
4. [학술지(정기간행물)] - L.J. Yang / 1992 / The effects of process variables on the bead height of submerged-arc weld deposits / Candian Metallurgical Quarterly 31 (4) : 289 ~ 297
5. [단행본] - David E. Gldberg / 1989 / Genetic Algorithms in Search, Optimization and Machine Learning / Addison-Wesley
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