Real-time nondestructive monitoring of the gas tungsten arc welding (GTAW) process by combined airborne acoustic emission and non-contact ultrasonics

Zhang, Lu; Basantes-Defaz, Alexandra-Del-Carmen; Abbasi, Zeynab; Yuhas, D. E.; Ozevin, Didem; Indacochea, Ernesto

doi:10.1117/12.2297455

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Another potential source of AE signals is melting and other material phase changes [42][43][44][45]. Based on the variety of sources for AE signals, this kind of signal offers the possibility for real-time process monitoring [39,41,46,47]. It is already described in the literature that measuring AE signals has the potential to detect and localize cracks [39][40][41].…”

Section: Acoustic Signalsmentioning

confidence: 99%

“…Arul et al mentioned that the changes in an AE signal during drilling processes can be used to monitor the sharpness of the drill and evaluate the optimal point of time for a tool change [1]. In addition to the monitoring of drilling processes, in the literature, it is described how AE signals can be useful for the quality monitoring of welding processes [46,52,53]. One research team conducted gas metalarc welding and used a combination of AE signals and other welding input parameters to predict the weld quality [52].…”

Section: Acoustic Signalsmentioning

confidence: 99%

“…This statement indicates that the choice of feature extraction for acoustic signals seems to be based on experience and domain understanding. In addition, Zhang et al used time-driven AE signals as a passive NDT method to describe the gas tungsten-arc welding process and make a statement about weld quality [46]. In the context of their paper, weld quality is defined as the existence or non-existence of holes (burned through defects) in the welding part [46].…”

Section: Acoustic Signalsmentioning

confidence: 99%

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New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials

Görick,

Schuster,

Larsen

et al. 2023

JMMP

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Thermoplastic composites (TCs) enjoy high popularity in the field of engineering. Due to this popularity, there is a growing need to assemble this material with the help of fast and efficient joining processes. One joining process, which has seen increased use, is the process of ultrasonic welding. To make reliable statements about the quality of the joined material, some kind of quality assurance has to be made. In terms of ultrasonic spot welding, there are already some documented approaches for observing or predicting the joining quality, but some of these most promising parameters for quality assurance are difficult to measure in the process of continuous ultrasonic welding. This is why new parameters are investigated for their potential to improve the prediction of ultrasonic-welded TCs’ quality. Thermography and sound emission data have been found to have a correlation with the produced weld quality and are fed into different machine learning algorithms. Despite the relatively small dataset, trained algorithms reach binary classification rates of over 90%, indicating that the newly discovered parameters show the potential to improve the quality assurance of ultrasonic-welded TCs in the future. This improvement may enable the establishment of the ultrasonic welding of TCs in manufacturing.

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