“…An analysis of the echo patterns (echoes' position, decay rate, etc.) provides the information about the presence of defects, insufficient nugget size as well as about significant changes in the microstructure [7,8]. The sensitivity of the technique is, however, not considered as ideal, and therefore alternative approaches have been investigated for years.…”
Section: Overview Of Ndt Methods and Techniques Currently Used For Rswmentioning
confidence: 99%
“…Other NDT methods were also used in laboratories (e.g. electromagnetic, micromagnetic, 3MA [8]), but they didn't reach industrial-level use so far.…”
Section: Overview Of Ndt Methods and Techniques Currently Used For Rswmentioning
Resistance spot welding (RSW) is one of the main joining technologies of thin sheets in the automotive industry. Key factors affecting the strength of the RSW joint are the nugget diameter, asymmetry, expulsions, intended surfaces, and the presence of cracks. Despite its broad use, the RSW joint quality verification is limited only to destructive testing and a limited number of non-destructive testing (NDT) methods. Most of the testing is done destructively by sampling, which assesses only systematic defects. Ultrasonic Testing (UT) is the most used NDT method to detect non-systematic defects in the RSW joints, however the probability of the defect detection of conventional testing techniques is not fully satisfactory. Other approaches were invented to deal with this situation. The article uses the currently most used NDT approach of the UT testing as a benchmark to evaluate the ability of thermographic testing to detect defective welds of the resistance spot welding in an on-line mode. The article demonstrates that the infrared thermography may find systematic process errors that are not detectable by other NDT methods by an analysis of different temperature drops measured after a constant delay time caused by different cooling dynamics of satisfactory and non-satisfactory weld joints.
“…An analysis of the echo patterns (echoes' position, decay rate, etc.) provides the information about the presence of defects, insufficient nugget size as well as about significant changes in the microstructure [7,8]. The sensitivity of the technique is, however, not considered as ideal, and therefore alternative approaches have been investigated for years.…”
Section: Overview Of Ndt Methods and Techniques Currently Used For Rswmentioning
confidence: 99%
“…Other NDT methods were also used in laboratories (e.g. electromagnetic, micromagnetic, 3MA [8]), but they didn't reach industrial-level use so far.…”
Section: Overview Of Ndt Methods and Techniques Currently Used For Rswmentioning
Resistance spot welding (RSW) is one of the main joining technologies of thin sheets in the automotive industry. Key factors affecting the strength of the RSW joint are the nugget diameter, asymmetry, expulsions, intended surfaces, and the presence of cracks. Despite its broad use, the RSW joint quality verification is limited only to destructive testing and a limited number of non-destructive testing (NDT) methods. Most of the testing is done destructively by sampling, which assesses only systematic defects. Ultrasonic Testing (UT) is the most used NDT method to detect non-systematic defects in the RSW joints, however the probability of the defect detection of conventional testing techniques is not fully satisfactory. Other approaches were invented to deal with this situation. The article uses the currently most used NDT approach of the UT testing as a benchmark to evaluate the ability of thermographic testing to detect defective welds of the resistance spot welding in an on-line mode. The article demonstrates that the infrared thermography may find systematic process errors that are not detectable by other NDT methods by an analysis of different temperature drops measured after a constant delay time caused by different cooling dynamics of satisfactory and non-satisfactory weld joints.
“…The most important factor affecting TSS is the size of weld nugget [36,37], which is the weld metal of the RSW joint and is formed from the solidification of the molten metal after a heating by Joule effect [38] and hence has an as-cast dendritic microstructure (Fig. 1).…”
This work aims to study the effect of Widmanstätten h phase on tensile shear strength (TSS) of resistance spot welding joints (RSW) of A286 superalloy subjected to post-weld high temperature aging treatment. The tensile shear test specimens were welded in the solution treated condition, and then subjected to six different post-weld aging treatments (at an aging temperature of 840°C for six different aging times). The ascast dendritic microstructure of the weld nugget and the austenite equiaxed-grain microstructure of the base metal have a different response to the post-weld high temperature aging treatment. Growth of Widmanstätten h phase in the weld nugget is faster than in the base metal. While in the base metal the Widmanstätten h phase precipitates into the grain, in the weld nugget precipitates at the interdendritic region because of the segregation of Ti towards the last-to-solidify interdendritic regions (studied by performing SEM/EDX analysis). Although the hardness increases with the presence of Widmanstätten h phase, the increase in hardness does not necessarily imply an increase in the experimental TSS.
“…18 The nugget is formed from the solidification of the molten metal and has a cast microstructure with coarse and columnar grains. 4,19 The human operator uses the ultrasonic testing to classify the 330 RSW joints into four categories ( Figure 1) according to the effect of the weld nugget on the ultrasonic beam: 4,19 (i) good weld (acceptable quality level): 123/330; (ii) undersize weld (unacceptable quality level): 86/330; (iii) stick weld (unacceptable quality level): 13/330; (iv) no weld (unacceptable quality level): 68/330.…”
In this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification of resistance spot welding joints. Instead of using the result of a non-destructive testing technique as input variables, classifiers are trained directly with the relevant welding parameters, i.e. welding current, welding time and the type of electrode (electrode material and treatment). The algorithms are compared in terms of accuracy and area under the receiver operating characteristic (ROC) curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overall.
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