Failure assessment of Mash Seam Weld breakage and development of online weld inspection system for early detection of weld failure

Reddy, Bonikila Pradeep; Indimath, Shivanandan; Shajan, Nikhil

doi:10.1016/j.engfailanal.2021.105967

Cited by 10 publications

(1 citation statement)

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“…This approach was later extended to also include 3D laser scanning features [8]. The third work describes the use of an ultrasonic probe with an integrated water chamber that is passed over new welds [9]. The resulting measurements are used to estimate the thickness of the weld; a thickness threshold then classifies the weld as healthy or faulty.…”

Section: Related Literaturementioning

confidence: 99%

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

et al. 2022

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Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven techniques are used to detect and replace poor quality welds in real-time. Statistical models are commonly used to address this problem as they use data readily available from the welding machine and require no specialized equipment. While successful in finding anomalies, these statistical models do not provide insight into the underlying process and are slow to adapt to changes in the machine’s or material’s behavior. We combine knowledge-based and data-driven techniques to create an incremental grey-box welding current prediction model for detecting anomalous welds, resulting in a powerful and interpretable model. In this work, we detail our approach and show evaluation results on industrial welding data collected over a period of 15 months containing behavioral shifts attributed to machine maintenance. Due to its incremental nature, our model resulted in two-thirds fewer rejected welds compared to statistical models, thus greatly reducing production overhead. Grey-box modeling can be applied to other welding features or domains and results in models that are more desirable for the industry.

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Section: Related Literaturementioning

confidence: 99%