Data Assimilation in the Welding Process for Analysis of Weld Toe Geometry and Heat Source Model

Shiraiwa, Takayuki; Enoki, Manabu; Goto, Sota; Hiraide, Takashi

doi:10.2355/isijinternational.isijint-2019-720

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…The parameters of the double ellipsoidal power density model were calibrated using both the temperature measured by the thermography camera and the cross-sectional bead shape observed by SEM. In this study, as a first step, the parameters

and

in Equation (2) were assumed to be a function of

as in reference [ 16 , 23 ]. Some researchers reported the calibration of the shape parameters by observing the top surface of the melting pool during a laser scan using high-speed imaging [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…In this study, it is assumed that

, and

. These assumptions are common in the field of welding (e.g., reference [ 23 ]) and additive manufacturing (e.g., reference [ 16 ]).…”

Section: Thermal Analysismentioning

confidence: 99%

“…In this study, it is assumed that A 1 = A 2 = A, c 1 = a, and c 2 = 2a. These assumptions are common in the field of welding (e.g., reference [23]) and additive manufacturing (e.g., reference [16]). Figure 3 shows the 2D models for the single and multiple laser scanning and the 3D model for the single laser scanning.…”

Section: Thermal Analysismentioning

confidence: 99%

“…For example, using Bayesian optimization, Kitano et al [7,8] have successfully found an optimal laser irradiation conditions to avoid defects and cracks in SLMed nickel superalloys. Shiraiwa et al [23] efficiently determined the heat source parameters in welding simulation using Bayesian optimization. In order to evaluate the accuracy of the calculation results and to improve it, the SLM process simulation also needs to be iterated many times to find the optimal heat source model parameters.…”

Section: Introductionmentioning

confidence: 99%

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Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using Bayesian Optimization

2021

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Selective laser melting (SLM) produces a near-net-shaped product by scanning a concentrated high-power laser beam over a thin layer of metal powder to melt and solidify it. During the SLM process, the material temperature cyclically and sharply rises and falls. Thermal analyses using the finite element method help to understand such a complex thermal history to affect the microstructure, material properties, and performance. This paper proposes a novel calibration strategy for the heat source model to validate the thermal analysis. First, in-situ temperature measurement by high-speed thermography was conducted for the absorptivity calibration. Then, the accurate simulation error was defined by processing the cross-sectional bead shape images by the experimental observations and simulations. In order to minimize the error, the optimal shape parameters of the heat source model were efficiently found by using Bayesian optimization. Bayesian optimization allowed us to find the optimal parameters with an error of less than 4% within 50 iterations of the thermal simulations. It demonstrated that our novel calibration strategy with Bayesian optimization can be effective to improve the accuracy of predicting the temperature field during the SLM process and to save the computational costs for the heat source model optimization.

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and

in Equation (2) were assumed to be a function of

Section: Discussionmentioning

confidence: 99%

“…In this study, it is assumed that

, and

. These assumptions are common in the field of welding (e.g., reference [ 23 ]) and additive manufacturing (e.g., reference [ 16 ]).…”

Section: Thermal Analysismentioning

confidence: 99%

Section: Thermal Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using Bayesian Optimization

2021

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“…Data assimilation had achieved great success, mainly in geophysics, in the early stage [ 48 ]. Recently, data assimilation has appeared in the field of materials science [ 49 , 50 , 51 , 52 , 53 , 54 ], including grain growth [ 49 , 50 ], solidification [ 51 , 52 ], welding [ 53 ], and fatigue crack propagation [ 54 ]. These studies shed light on the high potential of the data assimilation approach for the estimation of unmeasurable states and/or parameters during materials processing.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Data Assimilation of Temperature Dependence of Solid–Liquid Interfacial Properties of Nickel

et al. 2021

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Temperature dependence of solid–liquid interfacial properties during crystal growth in nickel was investigated by ensemble Kalman filter (EnKF)-based data assimilation, in which the phase-field simulation was combined with atomic configurations of molecular dynamics (MD) simulation. Negative temperature dependence was found in the solid–liquid interfacial energy, the kinetic coefficient, and their anisotropy parameters from simultaneous estimation of four parameters. On the other hand, it is difficult to obtain a concrete value for the anisotropy parameter of solid–liquid interfacial energy since this factor is less influential for the MD simulation of crystal growth at high undercooling temperatures. The present study is significant in shedding light on the high potential of Bayesian data assimilation as a novel methodology of parameter estimation of practical materials an out of equilibrium condition.

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Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning

Kusano,

Watanabe

2024

Integr Mater Manuf Innov

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To understand the correlation between process, structures, and properties in laser powder bed fusion (L-PBF), it is essential to use numerical analysis as well as experimental approaches. A finite element thermal analysis uses a moving heat source model represented as a volumetric heat flux to simulate heat input by laser. Because of its computational efficiency, finite element thermal analysis is suitable for iterative procedures such as parametric study and process optimization. However, to obtain valid simulated results, the heat source model must be calibrated by comparison with experimental results for each laser scanning condition. The need for re-calibration limits the applicable window of laser scanning conditions in the thermal analysis. Thus, the current study developed a novel heat source model that is valid and precise under any laser scanning condition within a wide process window. As a secondary objective in the development, we quantitatively evaluated and compared the four heat source models proposed to date. It was found that the most suitable heat source model for the L-PBF is conical one among them. Then, a multiple linear regression analysis was performed to represent the heat source model as a function of laser power and scanning velocity. Consequently, the thermal analysis with the novel model is valid and precise within the wide process window of L-PBF.

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Data Assimilation in the Welding Process for Analysis of Weld Toe Geometry and Heat Source Model

Cited by 8 publications

References 35 publications

Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using Bayesian Optimization

Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using Bayesian Optimization

Bayesian Data Assimilation of Temperature Dependence of Solid–Liquid Interfacial Properties of Nickel

Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning

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