Heat and fluid flow in additive manufacturing—Part I: Modeling of powder bed fusion

By T. Mukherjee, H. Wei, Amitava De, T. DebRoy. Computational materials science, 2018

182 citations recorded by the source corpus.

Abstract Structure and properties of components made by the powder bed fusion (PBF) additive manufacturing (AM) are often optimized by trial and error. This procedure is expensive, time consuming and does not provide any assurance of optimizing product quality. A recourse is to build, test and utilize a numerical model of the process that can estimate the most important metallurgical variables from the processing conditions and alloy properties. Here we develop and test a three-dimensional, transient, heat transfer and fluid flow model to calculate temperature and velocity fields, build shape and size, cooling rates and the solidification parameters during PBF process. This model considers temperature dependent properties of the powder bed considering powder and shielding gas properties, packing efficiency and powder size. A rapid numerical solution algorithm is developed and tested to calculate the metallurgical variables for large components fabricated with multiple layers and hatches rapidly. Part I of this article describes the model, solution methodology, powder bed properties, and model validation. The applications of the model for four commonly used alloys are presented in part II.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9d2b2c07c4417a546e0dfa33a0d388b739127291

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In Automotive Engineering

1. T. Mukherjee
2. T. DebRoy
3. Jinhui Yan
4. Qiming Zhu
5. Zeliang Liu
6. Amitava De
7. H. Wei
8. Wei Zhang

In Mechanical Engineering

1. T. Mukherjee
2. T. DebRoy
3. Jinhui Yan
4. Qiming Zhu
5. Zeliang Liu
6. H. Wei
7. Amitava De
8. Gerald L. Knapp

Citations in the corpus

• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (2020, background): “[11,12] developed a coupled thermal-fluid model to simulate directed energy deposition (DED) and laser powder bed fusion (LPBF) processes.”
• Mitigation of lack of fusion defects in powder bed fusion additive manufacturing (2018, methodology): “More details about the implementation of this model for L-PBF are described in our previous publication [24] and are not repeated here.”