On-line quality assessment has become one of the most critical requirements for improving
the efficiency and the autonomy of automatic resistance spot welding (RSW) processes. An
accurate and efficient model to perform non-destructive quality estimation is an essential
part of the assessment process. This paper presents a structured and systematic approach
developed to design an effective ANN-based model for on-line quality assessment in RSW.
The proposed approach examines welding parameters and conditions known to have an
influence on weld quality, and builds a quality estimation model step by step. The
modeling procedure begins by examining, through a structured experimental design, the
effect of welding parameters (welding time, welding current, electrode force and sheet
metal thickness) and welding conditions represented by typical characteristics of the
dynamic resistance curves on multiple welding quality indicators (indentation depth,
nugget diameter and nugget penetration) and by analyzing their interactions and their
sensitivity to the variation of the dynamic process conditions. Using these results and by
combining an efficient modeling planning method, neural network paradigm, multi-criteria
optimization and various statistical tools, the identification of the model form and the
variables to be included in the model is achieved by executing a systematic model
optimization procedure. The results demonstrate that the proposed approach can lead to a
general ANN-based model able to accurately and reliably provide an appropriate assessment
of the weld quality under diverse and variable welding conditions.