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Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components

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Abstract
The objective of this research is to define an optimized strategy for wire arc additive manufacturing (WAAM) process with a numerical tool. In this paper, a three-dimensional scaled steel plate model (120 mm x 24 mm x 6 mm) is built for numerical simulations to replace the expensive physical WAAM experiments. A series of thermo-metallo-mechanical analyses is conducted by using ABAQUS CAE user subroutines in which, thermal, metallurgical and mechanical models for gas metal arc welding (GMAW) process are implemented. Groups of variables in WAAM process namely wire melting current, voltage and torch moving speeds, as well as interval layer cooling coefficients, are chosen as input parameters of the artificial neural network (ANN). The two targeted values are hardness and ultimate strength of the steel plate model, which are obtained by fully coupled thermo-metallo-mechanical simulations or by quick results from commercial software JmatPro, and by real measurements on the test samples of WAAM experimental plates. In this primary study, Taguchi algorithm is used for data preparations of the numerical experiments. The tests are fulfilled with Neural Networks (NN) tool in MATLAB. A typical three-layer feedforward network is used for general works. The developed models show good prediction capability for hardness and ultimate strength. The ANN algorithm presents high potential in bidirectional modeling to develop the WAAM strategy tool. It is possible to realize the inverse modeling from properties obtained by fractions of phase volume to WAAM processing parameters.
Keywords
Hardware and Architecture, Modeling and Simulation, Software, WAAM, Additive manufacturing, Inverse modelling, FEM simulation, ANN, Machine learning, HEAT, TRANSFORMATION, MODEL, PARAMETERS, KINETICS, FERRITE, FLOW

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Citation

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MLA
Ling, Yong, et al. “Numerical Prediction of Microstructure and Hardness for Low Carbon Steel Wire Arc Additive Manufacturing Components.” SIMULATION MODELLING PRACTICE AND THEORY, vol. 122, 2023, doi:10.1016/j.simpat.2022.102664.
APA
Ling, Y., Ni, J., Antonissen, J., Ben Hamouda, H., Vande Voorde, J., & Abdel Wahab, M. (2023). Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components. SIMULATION MODELLING PRACTICE AND THEORY, 122. https://doi.org/10.1016/j.simpat.2022.102664
Chicago author-date
Ling, Yong, Junyan Ni, Joachim Antonissen, Haithem Ben Hamouda, John Vande Voorde, and Magd Abdel Wahab. 2023. “Numerical Prediction of Microstructure and Hardness for Low Carbon Steel Wire Arc Additive Manufacturing Components.” SIMULATION MODELLING PRACTICE AND THEORY 122. https://doi.org/10.1016/j.simpat.2022.102664.
Chicago author-date (all authors)
Ling, Yong, Junyan Ni, Joachim Antonissen, Haithem Ben Hamouda, John Vande Voorde, and Magd Abdel Wahab. 2023. “Numerical Prediction of Microstructure and Hardness for Low Carbon Steel Wire Arc Additive Manufacturing Components.” SIMULATION MODELLING PRACTICE AND THEORY 122. doi:10.1016/j.simpat.2022.102664.
Vancouver
1.
Ling Y, Ni J, Antonissen J, Ben Hamouda H, Vande Voorde J, Abdel Wahab M. Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components. SIMULATION MODELLING PRACTICE AND THEORY. 2023;122.
IEEE
[1]
Y. Ling, J. Ni, J. Antonissen, H. Ben Hamouda, J. Vande Voorde, and M. Abdel Wahab, “Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components,” SIMULATION MODELLING PRACTICE AND THEORY, vol. 122, 2023.
@article{8772243,
  abstract     = {{The objective of this research is to define an optimized strategy for wire arc additive manufacturing (WAAM) process with a numerical tool. In this paper, a three-dimensional scaled steel plate model (120 mm x 24 mm x 6 mm) is built for numerical simulations to replace the expensive physical WAAM experiments. A series of thermo-metallo-mechanical analyses is conducted by using ABAQUS CAE user subroutines in which, thermal, metallurgical and mechanical models for gas metal arc welding (GMAW) process are implemented. Groups of variables in WAAM process namely wire melting current, voltage and torch moving speeds, as well as interval layer cooling coefficients, are chosen as input parameters of the artificial neural network (ANN). The two targeted values are hardness and ultimate strength of the steel plate model, which are obtained by fully coupled thermo-metallo-mechanical simulations or by quick results from commercial software JmatPro, and by real measurements on the test samples of WAAM experimental plates. In this primary study, Taguchi algorithm is used for data preparations of the numerical experiments. The tests are fulfilled with Neural Networks (NN) tool in MATLAB. A typical three-layer feedforward network is used for general works. The developed models show good prediction capability for hardness and ultimate strength. The ANN algorithm presents high potential in bidirectional modeling to develop the WAAM strategy tool. It is possible to realize the inverse modeling from properties obtained by fractions of phase volume to WAAM processing parameters.}},
  articleno    = {{102664}},
  author       = {{Ling, Yong and Ni, Junyan and Antonissen, Joachim and Ben Hamouda, Haithem and Vande Voorde, John and Abdel Wahab, Magd}},
  issn         = {{1569-190X}},
  journal      = {{SIMULATION MODELLING PRACTICE AND THEORY}},
  keywords     = {{Hardware and Architecture,Modeling and Simulation,Software,WAAM,Additive manufacturing,Inverse modelling,FEM simulation,ANN,Machine learning,HEAT,TRANSFORMATION,MODEL,PARAMETERS,KINETICS,FERRITE,FLOW}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components}},
  url          = {{http://doi.org/10.1016/j.simpat.2022.102664}},
  volume       = {{122}},
  year         = {{2023}},
}

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