Functional clustering methods for resistance spot welding process data in the automotive industry

In the automotive industry, quality assessment of resistance spot welding (RSW) joints of metal sheets is typically based on costly and lengthy offline tests, which are unfeasible in the full-scale production on a large scale. However, the massive industrial digitalization triggered by the Industry 4.0 framework makes online measurements of RSW process parameters available for every joint produced. Among these, the so-called dynamic resistance curve (DRC) is recognized as the full technological signature of the spot welds. Motivated by this context, this article intends to show the potentiality and practical applicability of clustering methods to data represented by curves, and, in general, to functional data. In this way, the task of separating DRCs into homogeneous groups pertaining to spot welds with common mechanical and metallurgical properties can be performed without the need for arbitrary and problem-specific feature extraction. We provide a hands-on overview of the most promising functional clustering methods, and apply them to DRCs collected during RSW lab tests at Centro Ricerche Fiat. The identified groups of DRCs emerge to be strictly linked with the wear status of the electrodes, which, in turn, is conjectured to impact the RSW joint final quality. The analysis code, developed in the software environment R, accompanied by an essential tutorial and the ICOSAF project data set containing DRC measurements, are openly available online at .

Automated summary

This article explores the potential and practical applicability of clustering methods to curve data and functional data, demonstrating the ability to separate DRCs into homogeneous groups without the need for arbitrary and problem-specific feature extraction.