Nondestructive testing of resistance spot welds using eddy current thermography

Taram, Abdoulaye; Roquelet, Cyrielle; Meilland, Philip; Dupuy, Thomas; Kaczynski, Christine; Bodnar, Jean-Luc; Duvaut, Thierry

doi:10.1364/ao.57.000d63

Cited by 13 publications

(2 citation statements)

References 32 publications

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“…Excluding volumetric cracking measurement means that planar measurement is the only viable option to characterize cracking distribution. Many studies have assessed cracking susceptibility from plan view (as seen from the top of the weld) using methods such as optical microscopy [1,15,16], liquid dye penetrant [2,17], radiography [18], fluorescent magnetic particle detection [19] and pulsed eddy current thermography [20]. However, using these techniques only views the weld surface.…”

Section: Introductionmentioning

confidence: 99%

Significance of cutting plane in liquid metal embrittlement severity quantification

DiGiovanni

Hawkins

et al. 2021

SN Appl. Sci.

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The automotive industry is turning to advanced high strength steels (AHSS) to reduce vehicle weight and increase fuel efficiency. However, the zinc coating on AHSS can cause liquid metal embrittlement (LME) cracking during resistance spot welding. To understand the problem, the severity of the cracking must be measured. Typically, this is done from the weld cross-section. Currently, there is no standard procedure to determine which plane through the weld must be examined to gauge cracking severity, leading to a variety of practices for choosing a cutting plane. This work compares the magnitude and variability of LME severity measured from the plane of exhibiting the most severe surface cracking to arbitrarily chosen planes. The plane exhibiting the most severe cracks had more and longer cracks on the cross-section than the arbitrarily chosen plane, resulting in a higher crack severity measurement. This higher absolute measurement increased the relative accuracy of the examination, allowing for fewer welds to be examined to precisely determine the effect of LME mitigation methods on cracking severity, how welding parameters affect LME cracking severity and the predicted LME affected strength of a particular weld.

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Section: Introductionmentioning

confidence: 99%

Significance of cutting plane in liquid metal embrittlement severity quantification

DiGiovanni

Hawkins

et al. 2021

SN Appl. Sci.

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“…In recent days, various Meta heuristic algorithm like PSO (Particle Swarm Optimization), GA (Genetic Algorithm), ANN (Artificial Neural Network) and BPNN (Back Propagation Neural Network) have also been applied to find desired process parameters. Currently robotic welding systems are becoming increasingly popular and tend to substitute more and more mechanized and automated welding [17][18][19][20][21][22][23][24][25]. Robotic welding equipment can be programmed for different weldments.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

Ravichandran¹,

Meenakshipriya²,

Parameshwaran³

et al. 2020

Preprint

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The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.

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Infrared (IR) quality assessment of robotized resistance spot welding based on machine learning

Stavropoulos

Sabatakakis

Papacharalampopoulos

et al. 2021

Int J Adv Manuf Technol

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Nondestructive testing of resistance spot welds using eddy current thermography

Cited by 13 publications

References 32 publications

Significance of cutting plane in liquid metal embrittlement severity quantification

Significance of cutting plane in liquid metal embrittlement severity quantification

Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

Infrared (IR) quality assessment of robotized resistance spot welding based on machine learning

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