Simulation of molten pool dynamics and stability analysis in laser buttonhole welding

Cho, Won Ik; Woizeschke, Peer; Schultz, Villads

doi:10.1016/j.procir.2018.08.042

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…These higher amplitudes at high frequencies indicate great accelerations and oscillation speeds of the melt pool, which correlates with a high degree of pool dynamics compared to stable buttonhole welding. This is confirmed by the camera observations ( Figure 4) and CFD simulations in Reference [26], which showed that buttonhole welding significantly reduces the melt pool dynamics. Outstanding peaks in the frequency spectra of an unstable buttonhole are much lower than for other conditions; see Figure 5 at P L = 3 kW.…”

Section: Influence Of Melt Pool Dynamics On the Surface Qualitysupporting

confidence: 73%

“…The process was simulated using computational fluid dynamics (CFDs) in Reference [25], whereby the wire was "chopped" by the transverse oscillating laser, which correlated with the oscillation frequency. In Reference [26], simulations showed that the buttonhole calms the melt pool dynamics behind significantly which can be correlated with surface quality.…”

Section: Introductionmentioning

confidence: 99%

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High Seam Surface Quality in Keyhole Laser Welding: Buttonhole Welding

Schultz

Woizeschke

2018

JMMP

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Keyhole laser welding experiments with 1.5 mm thick aluminum sheets (EN AW-6082) were carried out with transversal beam oscillation and wire feeding. A circular cavity, which was named buttonhole, formed directly behind the laser spot at certain oscillation frequencies. The welding states “no buttonhole”, “unstable buttonhole”, and “stable buttonhole” were distinguished. The melt pool dynamics were experimentally analyzed and correlated with the resulting roughness and waviness of the seam surfaces. Criteria for stable buttonhole welding were derived. On the basis of the cavity radii relations, it is shown that capillary pressure conditions can explain the movement of the buttonhole with the process.

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Section: Influence Of Melt Pool Dynamics On the Surface Qualitysupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

High Seam Surface Quality in Keyhole Laser Welding: Buttonhole Welding

Schultz

Woizeschke

2018

JMMP

Self Cite

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“…Table 4 presents the case studies of laser welding modeling, in a systematic, synthesized, and chronological summary. Likewise, the categories that were observed in the studies were physical model, mathematical model, work descriptors, and case study, as well as whether it is a thermomechanical or multi-physical study and the type of software used in the simulation (Cho et al, 2012;Beiranvand et al, 2018;Cho et al, 2018;Ali et al, 2021;Ebrahimi et al, 2021;Halm et al, 2021;Ebrahimi et al, 2022;Examilioti et al, 2022;Jia et al, 2022;Mohan et al, 2022;Xue et al, 2022;Zhang et al, 2022).…”

Section: Cfd Tool For Engineering Analysismentioning

confidence: 99%

Numerical simulations and mathematical models in laser welding: a review based on physics and heat source models

Jiménez-Xamán,

Hernández-Hernández,

Tariq

et al. 2024

Front. Mech. Eng.

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The dominant phenomenon in laser welding processes is heat transfer by conduction, making it crucial to gain insights into energy distribution within the heat-affected region, including the melt pool. Thermal analysis enables the description of thermo-mechanical, metallurgical aspects, and also addresses studies related to fluid flow and energy transfer. As research in welding processes has advanced, these models have evolved. This is why it is now efficient to use computational modeling techniques as it allows us to analyze the behavior of laser welding during the process. This underlines the importance of this work which has carried out an exhaustive theoretical literature review with the objective of classifying and describing the numerical simulations of laser welding based on the physics involved. In that sense, the mathematical models and strategies used in laser welding are explored in a general way. Therefore, two types of laser welding by conduction and deep penetration are defined from this point and they are categorized according to the phenomena involved in Model Heat Conduction and Model Integral Multiphysics. This comprehensive review article serves as a valuable resource for higher education students by providing a structured and detailed exploration of laser welding and its mathematical modeling. By classifying and describing numerical simulations based on the physics involved, it offers a framework for students to understand the complexities of this field. Additionally, this innovative approach to organizing and presenting research contributes to educational innovation by facilitating a more efficient and effective learning experience, helping students acquire the knowledge and research skills necessary for advancements in the laser welding domain.

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“…The use of this method possesses advantages such as cost savings and reduced difficulty in parameter measurement [10]. However, trade-offs need to be made between computational accuracy and computational time [11,12].…”

Section: Introductionmentioning

confidence: 99%

The Temperature Field Prediction and Estimation of Ti-Al Alloy Twin-Wire Plasma Arc Additive Manufacturing Using a One-Dimensional Convolution Neural Network

Pan,

Ye,

Xia

et al. 2024

Applied Sciences

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Plasma arc deposition as an additive manufacturing technology has unique advantages for producing parts with complex shapes through layer-by-layer deposition. It is critical to predict and control the temperature field during the production process due to the temperature distribution and gradients determining the properties and performance of the part. Numerical simulation approaches, such as the finite element method, which provides a large amount of data for machine learning modeling, thus reducing the overhead of experimental measurements, are widely used in machine learning. In this paper, we propose a neural network combined finite element method and process prediction workflow. A one-dimensional convolutional neural network model for predicting 2D temperature distribution is developed by training the collected data on the planar temperature field of titanium–aluminum twin-wire plasma arc additive manufacturing and the finite element method. The results show that the predicted temperature mean square error is only 0.5, with less than a 20 °C error in peak temperature and a relative error below 1%. The proposed transfer learning method achieves the same training loss and is 500 iterations faster than basic training, which improves the training speed by 25%. The current study confirms the accurate performance of the ML model and the effectiveness of the optimization method.

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Simulation of molten pool dynamics and stability analysis in laser buttonhole welding

Cited by 10 publications

References 8 publications

High Seam Surface Quality in Keyhole Laser Welding: Buttonhole Welding

High Seam Surface Quality in Keyhole Laser Welding: Buttonhole Welding

Numerical simulations and mathematical models in laser welding: a review based on physics and heat source models

The Temperature Field Prediction and Estimation of Ti-Al Alloy Twin-Wire Plasma Arc Additive Manufacturing Using a One-Dimensional Convolution Neural Network

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