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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.
more » « less- Award ID(s):
- 1937128
- PAR ID:
- 10542288
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Materials
- Volume:
- 17
- Issue:
- 17
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 4363
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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