Thermal Analysis in Wire Arc Additively Manufactured SS308L Walls via Numerical Simulations

Bui, Manh Cuong; Le, Van Thao; Ta, Dinh Xuan; Si, Dinh; Dinh, Duc Manh; Doan, Tat Khoa; Tran, Van Chau

doi:10.1007/978-3-030-99666-6_2

Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation

Le

¹

,

Bui

²

,

Pham

³

et al. 2023

Int J Adv Manuf Technol

2

0

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Among metallic additive manufacturing technologies, wire and arc-directed energy deposition (WADED) is recently adopted to manufacture large industrial components. In this process, controlling the temperature evolution is very important since it directly influences the quality of the deposited parts. Typically, the temperature history in WADED can be obtained through experiments and/or numerical simulations, which are generally time-consuming and expensive. In this research, we developed a robust surrogate model (SM) for predicting the temperature history in WADED based on the combination of machining learning (ML) and finite element (FE) simulation. The SM model was built to predict the temperature history in the WADED of single weld tracks. For this purpose, FE model was first developed and validated against experiments. This validated FE model is then used to generate the data to train the ML modes based on the feed-forward neural network (FFNN). The trained SM model can fast and accurately predict the temperature history in the cases which were not previously used for training with a very high accuracy of more than 99% and in a very short time with only 38 s (after being trained) as compared with 5 h for a FE model. The trained SM can be used for studies that require a large number of simulations such as uncertainty quantification or process optimization.

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Efficient prediction of thermal history in wire and arc-directed energy deposition combining machine learning and numerical simulation

Le

¹

,

Bui

²

,

Pham

³

et al. 2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

Among metallic additive manufacturing technologies, wire and arc-directed energy deposition (WADED) is recently adopted to manufacture large industrial components. In this process, controlling the temperature evolution is very important since it directly influences the quality of the deposited parts. Typically, the temperature history in WADED can be obtained through experiments and/or numerical simulations, which are generally time-consuming and expensive. In this research, we developed a robust surrogate model (SM) for predicting the temperature history in WADED based on the combination of machining learning (ML) and finite element (FE) simulation. The SM model was built to predict the temperature history in the WADED of single weld tracks. For this purpose, FE model was first developed and validated against experiments. This validated FE model is then used to generate the data to train the ML modes based on the feed-forward neural network (FFNN). The trained SM model can fast and accurately predict the temperature history in the cases which were not previously used for training with a very high accuracy of more than 99% and in a very short time with only 38 s (after being trained) as compared with 5 h for a FE model. The trained SM can be used for studies that require a large number of simulations such as uncertainty quantification or process optimization.

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Metallurgical Characterization of SS 316L Repurposed by Wire Plus Arc Additive Manufacturing

Vu

¹

,

Le

²

2023

Int. j. eng. technol. innov.

1

0

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This research aims to analyze the microstructures and mechanical characteristics of stainless steel (SS) 316L repurposed by wire plus arc additive manufacturing (WAAM). The SS 316L wire is deposited on a SS 316 substrate, which can be repurposed. This deposited material underwent optical microscopy, X-ray diffraction, and tensile test, and the results indicate that it features cellular and columnar dendrites at the bottom and equiaxial grains at the top. The tensile strength of the interface region between the deposited material (DM) and the base material (BM) is the highest (559 ± 4.16 MPa vs. 510 ± 4.93 MPa in DM and 540 ± 2.65 in BM), indicating that the BM and the deposited layers are strongly bonded. All the results from the defect observation, microstructures, and mechanical characteristics confirm the potential of the WAAM process for repurposing.

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Thermal Analysis in Wire Arc Additively Manufactured SS308L Walls via Numerical Simulations

Cited by 3 publications

References 6 publications

Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation

Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation

Efficient prediction of thermal history in wire and arc-directed energy deposition combining machine learning and numerical simulation

Metallurgical Characterization of SS 316L Repurposed by Wire Plus Arc Additive Manufacturing

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