Abstract
In this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relationship between the parameters causing strong nonlinearity should be considered. The analysis of variance-based Gaussian process regression (ANOVA-GPR) method was implemented to construct surrogate modeling, which can express nonlinearity including uncertainty of weld quality. Major parameters among several process parameters in this welding process can be extracted by use of this novel method. The weld quality used as a cost function in the optimization of process parameters is defined by characteristics related to penetration and bead shape, and the sequential quadratic programming (SQP) method was used to determine the optimal welding condition. This approach enabled sound weld quality at a high travel speed of 1.9 m/min, which is difficult to achieve in the hybrid-tandem GMAW process.
Highlights
Gas metal arc welding (GMAW) is a crucial manufacturing technology that affects the quality and productivity of car manufacturing, shipbuilding, and plant industries
The overall resultsnumber of this of experimental trials, the optimization using a surrogate model such as paper indicate that the mixed approach of ANOVA-Gaussian process regression (GPR) could be applied to the hybridcan make the search for theeffectively process parameters of the hybrid-tandem
The overall results of this paper indicate that the mixed approach of ANOVA-GPR could be applied to the hybrid-tandem GMAW and could effectively optimize the welding performance
Summary
Gas metal arc welding (GMAW) is a crucial manufacturing technology that affects the quality and productivity of car manufacturing, shipbuilding, and plant industries. I-groove GMAW that used a mild steel plate and ER70S-6 wire They proved the correlation between input parameters, such as wire feed rate, welding voltage, and welding speed, and output parameters, such as bead height, bead width, and penetration, via response surface methodology, and acquired an optimal welding condition using a genetic algorithm (GA). Our research proposes a methodology to optimize the hybrid-tandem welding process with a minimum number of experiments for cost reduction in the AH36 plate’s horizontal fillet welding This welding process has more parameters than the general welding process and generally shows high nonlinearity between inputs and outputs, so we use a novel analysis of variance-based Gaussian process regression method (ANOVA-GPR) to find the global optimum among several parameters. The proposed methodology using the ANOVA-GPR is verified by performing the hybrid-tandem experiment using the acquired optimal parameter
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