Your browser doesn't support javascript.
loading
A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition.
Wu, Sung-Heng; Tariq, Usman; Joy, Ranjit; Mahmood, Muhammad Arif; Malik, Asad Waqar; Liou, Frank.
Afiliación
  • Wu SH; Department of Mechanical Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
  • Tariq U; Department of Mechanical Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
  • Joy R; Department of Mechanical Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
  • Mahmood MA; Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO 65409, USA.
  • Malik AW; National Strategic Planning and Analysis Research Center (NSPARC), Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA.
  • Liou F; Department of Mechanical Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Materials (Basel) ; 17(17)2024 Sep 03.
Article en En | MEDLINE | ID: mdl-39274754
ABSTRACT
In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Materials (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Materials (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza