application of the extreme learning machine for modeling the bead geometry in gas metal arc welding process

Gas metal arc welding (gmaw) is a widespread process used for rapid prototyping of metallic parts. in this process, in order to obtain a desired welding geometry, it is very important to predict the weld bead geometry based on the input process parameters, which are voltage, wire feed rate, welding speed and welding nozzle angle. for this purpose, a global model of the welding geometry must be defined based on these parameters. due to the nonlinear and coupled multivariable relationship between the process parameters and the weld bead geometry, it is not possible to define this model in form of an explicit mathematical expression, and therefore application of supervised learning algorithms can be investigated as an efficient alternative in this problem. in this paper, application of the extreme learning machine (elm) and support vector machine (svm), as two efficient and powerful machine learning algorithms for predictive modelling of this process has been investigated and error analysis of the proposed models suggest that the output parameters of this process can be predicted by the elm algorithm with higher precision and generalization capability.