Real time Monitoring Method of Welding Defects Based on LSTM Sequence Model

Zhou, Xundao; Chen, Mingmin; Shang, W. G.; Shen, Hongping; Xu, Huawei

doi:10.1109/iciba56860.2023.10165203

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, proper analysis must be made, which is preceded with the cutting of samples, etching, etc. [ 23 , 24 , 25 ]. This high workload cannot be omitted by sample generation using, e.g., finite-element-method simulation.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

Górka,

Jamrozik,

Wyględacz

et al. 2024

Sensors

View full text Add to dashboard Cite

Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible.

show abstract

Section: Introductionmentioning

confidence: 99%