Instance selection-based dissimilar weldment design prediction for resistant spot welding

Abstract

Resistance spot welding (RSW) is a critical joining method in sheet-metal industries. The machine-learning technique fueled by the historical experimental data of the existing materials has been used to build the data-driven model (DDM). The DDM is expected to be a promising tool to investigate a new material and its welding behavior because DDM can narrow the range of the test matrix and can thus reduce the number of necessary physical experiments and the cost. However, one of crucial data quality problems with machine learning is that training data sets’ lack of descriptability for test sets causes poor prediction. This research starts by indicating that such data quality problems that exist in the context of weldment design. To resolve this problem, the presented study introduces a novel approach named Similar Weldment Case Selection (SWCS), which predicts the key parameter, the nugget size, of spot welding results of a new material by selecting the most similar one among the existing welding cases and then constructing a prediction model to generate the results. In order to overcome the difficulties with defining the selection criteria only with the material properties and geometric features, this study has come up with another factor, nugget-size weld-current series (NWS), to consider; the NWS is a factor that describes the shape of the relation between weld-current and nugget size. The similarity between two NWSs of different materials is calculated (quantified) with the dynamic time warping (DTW) method. Initially, the twelve conventional algorithms are tested for varying degrees of descriptability between the two weldment designs for test and train datasets; the prediction accuracies are found to be proportional to the train set’s descriptability on the test set. The results are then compared with those from the SWCS. The SWCS yields superior accuracy than the twelve algorithms do when the two materials are similar or different. However, the superiority disappears when the two are the same.