An LSTM-based measurement method of 3D weld pool surface in GTAW

Li, Lidong; Cheng, Fangjie; Wu, Shaojie

doi:10.1016/j.measurement.2020.108809

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…This is the case because in simulations for fixed sets of parameters, the same results will always be obtained [ 26 , 27 , 28 ]. During welding for the same set of parameters, slightly different results are gathered due to differences in the material structure or temporary changes in process conditions [ 29 , 30 , 31 ].…”

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

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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.

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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

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show abstract

“…Recently, 3D measurement methods have played an important role in manufacturing, health care and agricultural industries, and are mainly used to obtain 3D models of measured objects for reverse engineering, quality inspection, and dimensional measurement [ 5 , 6 , 7 , 8 ]. 3D measurement methods can be divided into contact and non-contact measurement methods.…”

Section: Introductionmentioning

confidence: 99%

An Improved Shape from Focus Method for Measurement of Three-Dimensional Features of Fuel Nozzles

Hou

Zou

Zhang

et al. 2022

Sensors

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The precise three-dimensional measurement of fuel nozzles is of great significance to assess the manufacturing accuracy and improve the spray and atomization performance. This paper proposes an improved fast shape from focus (SFF) method for three-dimensional measurement of key features of fuel nozzles. In order to ensure the measurement accuracy and efficiency of the SFF, the dispersion of the measured points from a standard flat plane was used to select the optimal combination of the focus measure operator, window size and sampling step size. In addition, an approximate method for the focus measure interval is proposed to improve the measurement efficiency, which uses the peak region of the central pixel to replace the peak region of other pixels. The results show that the proposed method decreased the average computation time of the focus measure by 79.19% for the cone section and by 38.30% for the swirl slot. Compared with a reference laser scanning microscope, the measurement error in length is within 10 μm and the error in angle is within a maximum 0.15°.

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A measurement method for the forced weld pool surface in GTAW based on simulation model and LSTM network

Liu

Cheng

2022

Weld World

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An LSTM-based measurement method of 3D weld pool surface in GTAW

Cited by 9 publications

References 18 publications

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

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

An Improved Shape from Focus Method for Measurement of Three-Dimensional Features of Fuel Nozzles

A measurement method for the forced weld pool surface in GTAW based on simulation model and LSTM network

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