Automated heat source calibration for the numerical simulation of laser beam welded components

Belitzki, A.; Marder, C.; Huissel, A.; Zaeh, Michael F.

doi:10.1007/s11740-016-0664-9

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…On the other hand, a thermal FE simulation can be applied as an inverse problem to describe the influence of the process parameters on the melt pool dimensions. Belitzki et al [18] adapt the input parameters for FE simulations of melt pools using an optimization algorithm such that the shape of the simulated melt pool matches the measured one as closely as possible. This was done for laser beam welding.…”

Section: Discussionmentioning

confidence: 99%

A new approach for automated measuring of the melt pool geometry in laser-powder bed fusion

et al. 2021

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Additive manufacturing (AM) offers unique possibilities in comparison to conventional manufacturing processes. For example, complex parts can be manufactured without tools. For metals, the most commonly used AM process is laser-powder bed fusion (L-PBF). The L-PBF process is prone to process disturbances, hence maintaining a consistent part quality remains an important subject within current research. An established indicator for quantifying process changes is the dimension of melt pools, which depends on the energy input and the cooling conditions. The melt pool geometry is normally measured manually in cross sections of solidified welding seams. This paper introduces a new approach for the automated visual measuring of melt pools in cross-sections of parts manufactured by L-PBF. The melt pools are first segmented in the images and are then measured. Since the melt pools have a heterogeneous appearance, segmentation with common digital image processing is difficult, deep learning was applied in this project. With the presented approach, the melt pools can be measured over the whole cross section of the specimen. Furthermore, remelted melt pools, which are only partly visible, are evaluated. With this automated approach, a high number of melt pools in each cross-section can be measured, which allows the examination of trends over the build direction in a specimen and results in better statistics. Furthermore, deviations in the energy input can be estimated via the measured melt pool dimensions.

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Section: Discussionmentioning

confidence: 99%

A new approach for automated measuring of the melt pool geometry in laser-powder bed fusion

et al. 2021

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“…It should be noted that the total number of data points in each objective function was governed by the experimental temperature data collection. An equal number of data points was selected for both objective functions to ensure that the contribution of error to each function was not weighted due to an uneven number of data points [19].…”

Section: Figure 5 Schematic Of the Inverse Approach For (A) Calculatimentioning

confidence: 99%

“…Belitzki et al [19] determined that the initial guess, supplied to a gradient based optimisation algorithm, severely effects the final optimised results. This is because several local optima can exist for the same melt pool geometry.…”

Section: Initiation Of the Thermal Calibrationmentioning

confidence: 99%

“…From comparing the experimental and predicted thermal field, the maximum error for the peak temperatures was 6.97% and for some cases the cooling rate was overestimated. An automated solution procedure was presented by Belitzki et al [19], which utilised optimisation algorithms to define the geometrical parameters of a double ellipsoid heat source. The welding process was assumed to be quasi-stationary with a constant weld geometry.…”

Section: Introductionmentioning

confidence: 99%

“…A sufficient element size should be used which provides a solution independent of the mesh for the temperature field and subsequent melt pool geometry. Belitzki et al [19] conducted a brief mesh independence study for a manually calibrated heat source; however, the final element size was used for the subsequent optimisations, with no attempt to verify the initial element size when using the optimised result. An automated mesh refinement model for 3D FE simulation of welding was presented by Lindgren et al [20].…”

Section: Introductionmentioning

confidence: 99%

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An automated inverse method to calibrate thermal finite element models for numerical welding applications

Walker

Bennett

2019

Journal of Manufacturing Processes

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Numerical modelling of welding processes is often completed using a sequentially coupled FE thermo-mechanical analysis to predict both the thermal and mechanical effects induced by the process. The accuracy of the predicted residual stresses and distortions are highly dependent upon an accurate representation of the thermal field. Utilising this approach, the physics of the melt pool are replaced with a heat source model which represents the heat flux distribution of the process. Many heat source models exist; however, the parameters which define the geometrical distribution have to be calibrated using experimental data. Currently the most common method involves trial and error, until the predicted thermal history and melt pool geometry accurately represent the experimental data. Although this is a simple approach, it is often time dependant and inherently inaccurate. Therefore, this study presents an automated calibration process, which determines the optimum element size for the FE mesh and then refines the parameters of the heat source model using an inverse approach. The proposed procedure was implemented for laser beam welding, operating in both the conductive and keyhole regimes. To ensure that both the thermal history data and melt pool geometry were predicted with accuracy, a multi-objective optimisation was required. The proposed methodology was experimentally validated through welding nine IN718 samples using a Nd:YAG laser heat source. A good correlation between the experimental and numerical data sets were apparent. With regards to the predicted melt pool geometry, the maximum error for the width, depth and area of the melt pool was 8.4%, 4.0% and 11.0% respectively. The minimum error was 1.5%, 0.3% and 0.3% respectively. For the temperature profiles, the maximum and minimum error for the peak temperature was 8.6% and 1.2%. Overall, the proposed calibration procedure allows automation of an

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Improving the prediction accuracy of thermal finite element analysis for laser welding through an automated optimization method

Rong

2022

Int J Adv Manuf Technol

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Automated heat source calibration for the numerical simulation of laser beam welded components

Cited by 20 publications

References 9 publications

A new approach for automated measuring of the melt pool geometry in laser-powder bed fusion

A new approach for automated measuring of the melt pool geometry in laser-powder bed fusion

An automated inverse method to calibrate thermal finite element models for numerical welding applications

Improving the prediction accuracy of thermal finite element analysis for laser welding through an automated optimization method

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