A study on a representative heat source model for simulating laser welding for liquid hydrogen storage containers

When predicting welding deformation of the laser-manufactured vehicles and aerospaces, analytical solutions or empirical formulas are not usually accessible in complex problems. Based on the inherent deformation method, a machine learning (ML) approach for predicting welding deformation of welded structures is proposed based on an artificial neural network (ANN). This method is a promising substitute for analytical, empirical, and finite element (FE) solutions due to its accuracy, easy-to-use, efficiency, and universality. First, the outputs of the ANN are determined via dimensionless analysis and comparison of numerical results, which are dimensionally independent. Then, based on the inherent deformation method, the training and validation sets of the ANN are generated through an elastic finite element analysis. At last, the structure of the ANN is determined by analyzing the ANN prediction accuracy with different hidden layers, numbers of neurons, and activation functions. The results show that the ML solutions are in good agreement with the FE results, verifying the effectiveness and generalization ability of the proposed method.

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A Study on the Enhanced Process of Elaborate Heat Source Model Parameters for Flux Core Arc Welding of 9% Nickel Steel for Cryogenic Storage Tank

Pyo

Jeong

Kim

et al. 2022

JMSE

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Various regulations are being devised and implemented to prevent the environmental pollution that is threatening mankind. The International Maritime Organization has strengthened regulations on sulfur, a notorious pollutant, to prevent sea pollution. In addition, the production of LNG fueled ships is increasing. Among various metals, 9% nickel steel is widely used in the shipbuilding industry because it is advantageous in terms of material strength and cryogenic impact toughness. Various studies are being carried out to predict and prevent its distortion, caused by welding, in the design. To predict welding distortion during flux core arc welding, this study found a way to refine the parameters constituting the Goldak welding heat source. The optimal heat source parameters were derived by using BOP experiments, cross-sectional analysis, finite element analysis and global optimization algorithm. When re-analyzed and verified based on the values, an error of up to 6.3% was found between simulation results and experimental values. The process was improved by clarifying the objective function and reducing the range of candidate welding efficiencies during global optimization and the process efficiency was also improved by reducing analysis time with a simplified model. Therefore, it is thought that this study can contribute to the productivity improvement of LNG storage containers, helping engineers apply it immediately in the industrial field.

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A study on a representative heat source model for simulating laser welding for liquid hydrogen storage containers

Cited by 3 publications

References 36 publications

Welding Distortions Analysis Considering the Hardening Model, Deposition Processes, and Dissimilar Mechanical Behavior of the Base and Filler Metal

Welding Distortions Analysis Considering the Hardening Model, Deposition Processes, and Dissimilar Mechanical Behavior of the Base and Filler Metal

Prediction of laser-welded deformation using artificial neural networks

A Study on the Enhanced Process of Elaborate Heat Source Model Parameters for Flux Core Arc Welding of 9% Nickel Steel for Cryogenic Storage Tank

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