This paper is to represent new algorithms to predict process parameters on top-bead width in robotic gas metal arc（GMA） welding process.The models have been developed：linear, curvilinear and intelligent model based...This paper is to represent new algorithms to predict process parameters on top-bead width in robotic gas metal arc（GMA） welding process.The models have been developed：linear, curvilinear and intelligent model based on full factorial design with two replications.Regression analysis was employed for optimization of the coefficients of linear and curvilinear models, while genetic algorithm（GA） was utilized to estimate the coefficients of an intelligent model.Not only the fitting of these models were checked and compared by using a variance test（ANOVA）, but also the prediction on top-bead width using the developed models were carried out based on the additional experiments.The developed models were employed to investigate the characteristic between process parameters and top-bead width.Resulting solutions and graphical representation showed that the intelligent model developed can be employed for prediction of bead geometry in GMA welding process.展开更多
基金supported by the 2006 research funds from Mokpo National University
文摘This paper is to represent new algorithms to predict process parameters on top-bead width in robotic gas metal arc（GMA） welding process.The models have been developed：linear, curvilinear and intelligent model based on full factorial design with two replications.Regression analysis was employed for optimization of the coefficients of linear and curvilinear models, while genetic algorithm（GA） was utilized to estimate the coefficients of an intelligent model.Not only the fitting of these models were checked and compared by using a variance test（ANOVA）, but also the prediction on top-bead width using the developed models were carried out based on the additional experiments.The developed models were employed to investigate the characteristic between process parameters and top-bead width.Resulting solutions and graphical representation showed that the intelligent model developed can be employed for prediction of bead geometry in GMA welding process.
