Effect of Electrode Force on Fracture Type of DQSK Steel Resistance Spot Welds

Pouranvari, M.; Ranjbarnoode, Eslam

doi:10.12776/ams.v19i2.99

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Around the nugget, there exists a HAZ in the welded pieces whose microstructure can also be altered by the fast heating and cooling. Since the boundary of the HAZ is much clearer than the nugget zone and it shows a strong positive correlation to the nugget size (Pouranvari, 2011), the HAZ size can be another very useful indicators for welding quality estimation in practice. For those reasons, the HAZ size instead of the nugget size is chosen to be the main indicator for our schemes.…”

Section: Proposed Data-driven Framework For Quality Monitoring Of Spomentioning

confidence: 99%

“…Popular signals include electrical signals such as the dynamic current, voltage, power and resistance (Batista and Brandi, 2013; Cho and Rhee, 2000, 2002; Podžraj et al, 2006) and mechanical signals like the electrode force and electrode displacement. The signals can be used to estimate quality indicators such as the nugget size, heat affected zone (HAZ) size or mechanical strength via advanced data processing techniques (Pouranvari, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Quality monitoring of spot welding with advanced signal processing and data-driven techniques

Wang

Zhou

Yan

et al. 2017

Transactions of the Institute of Measurement and Control

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As resistance spot welding is a crucial and widely used metal joining technique nowadays, a cheap and highly accurate online quality monitoring scheme is strongly demanded in industry. In this paper, a novel framework for welding quality examination is proposed by using advanced signal processing and artificial intelligence techniques. Our proposed framework consists of two objective schemes, a general welding quality classification scheme and a more detailed and advanced welding quality estimation scheme. To achieve a fast, convenient and cheap monitoring strategy, only the easily obtained electrical signals are monitored for data acquisition. For the welding quality classification, a self-organizing map under a windowed feature extraction is applied. Results of the spot welding experiment from a portable welding machine show that the classification accuracy can be 92.9%. For welding quality estimation, variation of the welding time and detailed aspects of welding quality are introduced. In particular, a modified recurrent neural network is utilized for the size estimation of the heat affected zone, while a novel self-organizing map type classifier is used to detect the expulsion condition along with its occurrence time. During the spot welding experiments, the average error percentage of the heat affected zone size estimation is around 6.7% and the accuracy on expulsion detection reaches 93.3%.

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Section: Proposed Data-driven Framework For Quality Monitoring Of Spomentioning

confidence: 99%