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탄템 GMA 용접공정의 표면비드높이 예측을 위한 STACO모델 개발에 관한 연구
이종표,김일수,박민호,박철균,강봉용,심지연,Lee, Jongpyo,Kim, IllSoo,Park, Minho,Park, Cheolkyun,Kang, Bongyong,Shim, Jiyeon 대한용접접합학회 2014 대한용접·접합학회지 Vol.32 No.6
One of the main challenges of the automatic arc welding process which has been widely used in various constructions such as steel structures, bridges, autos, motorcycles, construction machinery, ships, offshore structures, pressure vessels, and pipelines is to create specific welding knowledge and techniques with high quality and productivity of the production-based industry. Commercially available automated arc welding systems use simple control techniques that focus on linear system models with a small subset of the larger set of welding parameters, thereby limiting the number of applications that can be automated. However, the correlations of welding parameters and bead geometry as welding quality have mostly been linked by a trial and error method to adjust the welding parameters. In addition, the systematic correlation between these parameters have not been identified yet. To solve such problems, a new or modified models to determine the welding parameters for tandem GMA (Gas Metal Arc) welding process is required. In this study, A new predictive model called STACO model, has been proposed. Based on the experimental results, STACO model was developed with the help of a standard statistical package program, MINITAB software and MATLAB software. Cross-comparative analysis has been applied to verify the reliability of the developed model.
STACO 모델을 이용한 탄템 GMA 용접공정의 표면비드 폭 예측
이종표(Jong Pyo Lee),박민호(Min Ho Park),김도형(Do Hyeong Kim),진병주(Byeong Ju Jin),손준식(Joon Sik Son),강봉용(Bong Yong Kang),심지연(Ji Yeon Shim),김일수(Ill Soo Kim) 한국생산제조학회 2016 한국생산제조학회지 Vol.25 No.1
Tandem arc welding is a guarantor for high efficiency and cost saving since the quantity of wire which is deposited in the welding is approximated 30% greater that in conventional welding. The welding process is now being successfully applied in many industries. However, in the case of tandem arc welding, good quality and high productivity should depend on the welding parameters. Therefore, an intelligent algorithms for the automatic tandem arc welding process has been necessarily required. In this study, a predictive model based on the neural network by using the data acquired during tandem gas metal arc (GMA) welding process has been developed. To verify the reliability of the developed predictive model, a mutual comparison with the surface of the top-bead width obtained from actual experiments has been analyzed.