On the nondestructive testing and monitoring of cracks in resistance spot welds: recent gained experience

Dahmene, Fethi; Yaacoubi, Slah; Mountassir, Mahjoub El; Bouzenad, Abd Ennour; Rabaey, Pierre; Masmoudi, Mohamed; Nennig, Pascal; Dupuy, Thomas; Benlatreche, Yacine; Taram, Abdoulaye

doi:10.1007/s40194-022-01249-w

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…In the literature, a number of studies have already proved that the process emissions include information regarding the joint formation and the generation of defects. However, not many studies have been focused on revealing internal defects in RSW such as cracks or voids [77] compared to similar applications for other welding applications [78].…”

Section: State Of Practice and State Of The Art: How They Affect Each...mentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

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Section: State Of Practice and State Of The Art: How They Affect Each...mentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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“…The development of monitoring solutions in Refill FSSW is a key factor for the deployment and acceptance of this process in industry. Acoustic emission (AE) [1] monitoring is the most promising solution that can address this challenge thanks to its history and experience in welding [ 2 , 3 ], which are inspirational. Despite the important interest of researchers on the use of AE in similar process such as friction stir welding (FSW) [ 4 , 5 ], the technology readiness level still below industrial expectations.…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission

et al. 2022

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“…NDT methods have become a focal point of research for identifying a good weld without resorting to destruction, incorporating techniques such as radiographic or ultrasonic inspections. However, these methods face challenges, including being timeconsuming, cost-ineffective, and susceptible to human and environmental factors [10,[12][13][14][15]. Futhermore, in [16], the problem of the corona bond is pointed out in ultrasonic testing; in fact, the corona bond, being welded but not melted as the nugget, has to be assessed during ultrasonic testing.…”

Section: Introductionmentioning

confidence: 99%

Nugget and corona bond size measurement through active thermography and transfer learning model

Santoro,

Razza,

De Maddis

2024

Preprint

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Resistance Spot Welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). The investigation starts by examining the phase and amplitude gradient of the thermal signal. A convolutional neural network (CNN) is employed to estimate the size of the nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study presents a suggestion to adopt a new methodology for automatic RSW quality control based on thermal signal analysis.

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On the nondestructive testing and monitoring of cracks in resistance spot welds: recent gained experience

Cited by 13 publications

References 28 publications

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission

Nugget and corona bond size measurement through active thermography and transfer learning model

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