An Ensemble Random Forest Model to Predict Bead Geometry in GMAW Process

Abstract

A prevalent method for rapid prototyping of metallic parts is gas metal arc welding (GMAW). As the input parameters impose a highly nonlinear impact on the weld bead geometry, precise estimation of the geometry is a complex problem. Therefore, in this study, a novel combination of the most powerful machine learning algorithms is selected to overcome the complexity of the problem and also reach an acceptable degree of precision. To this end, the hybrid combination of the support vector machine (SVM) and relevance vector machine (RVM) is developed based on the random forest (RF) ensemble learning approach. The models are established based on a global database of welding geometry, and the corresponding process parameters obtained are based on a set of experiments. Performance evaluation between RVM, SVM, and the proposed model was performed based on the coefficient of determination ([Formula: see text]) and the ratio of root means square error (RMSE) to the maximum measured outputs ([Formula: see text]/[Formula: see text]). The RF-based RVM-SVM model obtained 0.9725 and 0.8850 for [Formula: see text] and 0.0257 and 0.0447 for [Formula: see text]/[Formula: see text] in predicting the height and width of the bead, respectively. The result clearly showed the effectiveness of the proposed model in predicting the GMAW trend.