Prediction Modeling Framework With Bootstrap Aggregating for Noisy Resistance Spot Welding Data

Abstract

In resistance spot welding (RSW), data inconsistency is a well-known issue. Such inconsistent data are usually treated as noise and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for all design and manufacturing applications since data that are often considered noise can contain important information in determining weldment design, and proper welding conditions. In this paper, we present the Meta2 prediction framework to provide cost-effective opportunities for proper welding material and condition selection from the noisy RSW quality data. The Meta2 framework employs bootstrap aggregating with support vector regression (SVR) to improve the prediction accuracy on the noisy RSW data with computational efficiency. Hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling to reduce the computational cost. Experiments on three artificially generated noisy datasets and a real RSW dataset indicate that Meta2 is capable of providing satisfactory solutions with a noticeably reduced computational cost. The authors find Meta2 promising as a potential prediction model algorithm for this type of noisy data.