Dynamic Resistance Based Model for On-Line Resistance Spot Welding Quality Assessment

Abstract:

On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance (DR) gives a relative clear picture of the welding nugget formation and presents a significant correlation with the RSW quality indicators (QI). This paper presents a structured approach developed to design an effective DR-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 assessment model step by step. The modeling procedure begins by examining, through a structured experimental design, the relationships between welding parameters, typical characteristics of the RD curves and multiple welding QI. Using these results and various statistical tools, different integrated quality assessment models combining an assortment of DR attributes are developed and evaluated. The results demonstrate that the proposed approach can lead to a general model able to accurately and reliably provide an appropriate assessment of the weld quality under variable welding conditions.

• Periodical: Materials Science Forum (Volumes 706-709)
• Pages: 2925-2930
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.706-709.2925
• Online since: January 2012
• Authors: Abderrazak El Ouafi, R. Belanger, Michel Guillot
• Keywords: Artificial Neural Network (ANN), Design of Experiment (DOE), Dynamic Resistance, Resistance Spot Welding (RSW), Weld Quality Assessment

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