Weld Quality Inspection Based on Online Measured Indentation From Servo Encoder in Resistance Spot Welding

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.

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Online Weld Quality Benchmarking and Assurance during the Mass-Production Resistance Spot Welding Process

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.443-444.872 ◽

2012 ◽

Vol 443-444 ◽

pp. 872-880 ◽

Cited By ~ 2

Author(s):

Liang Gong ◽

Cheng Liang Liu ◽

Yan Ming Li ◽

Bing Chu Li

Keyword(s):

Quality Assessment ◽

Resistance Spot Welding ◽

Spot Welding ◽

Welding Process ◽

Bayesian Belief Networks ◽

Displacement Curve ◽

Weld Quality ◽

Probabilistic Forecasting ◽

Resistance Spot ◽

Continuous Quality

Nowadays online quality estimation for the resistance spot welding (RSW) has benefited a lot from monitoring the electrode displacement caused by nugget thermal expansion. Based on these emerging monitoring techniques a new approach is proposed to classify the weld quality and assure the quality for mass-produced weld group, which enables the continuous quality improvement concept during the welding process. A causal models are built with the offline trained Bayesian Belief Networks (BBN). It is a weld quality assessment net reveals the dependency of the weld quality on the features displayed by the displacement curve, which can be used for overdesigning the safety welds or as the probabilistic forecasting model for online weld quality assessment. The experimental results show that the proposed approach is valid and feasible to predict the weld quality and assure the overall quality for weld group in real applications.

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The Importance of Governing Metal Thickness for Resistance Spot Welding of Aluminium

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.473.319 ◽

2011 ◽

Vol 473 ◽

pp. 319-326

Author(s):

Li Han ◽

Martin Thornton ◽

Douglas Boomer ◽

M. Shergold

Keyword(s):

Resistance Spot Welding ◽

Spot Welding ◽

Joint Strength ◽

Loading Conditions ◽

Weld Quality ◽

Welding Quality ◽

Resistance Spot ◽

Lap Shear ◽

Metal Thickness ◽

Cross Tension

A study was carried out to investigate the effect of governing metal thickness (GMT) on weld quality and strength of resistance spot welded (RSW) AA5754 aluminium. Quasi-static joint strengths were evaluated for 27 different joint stack-ups in three test geometries: lap-shear, coachpeel and cross-tension; whilst micro examination was conducted on some of the samples to assess weld quality. The results derived from over 1000 samples show the importance of GMT and its various effects: the GMT has a significant effect on welding quality and joint strength by controlling the attainable weld diameter, regardless of stack-ups; depending on loading conditions, its effect may differ.

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