An Integrated Multiscale Model to Study the Marangoni Effect on Molten Pool and Microstructure Evolution

IF 2.4 3区材料科学 Q3 ENGINEERING, MANUFACTURING Integrating Materials and Manufacturing Innovation Pub Date : 2023-12-14 DOI:10.1007/s40192-023-00327-1

Chuanzhen Ma, Ruijie Zhang, Zixin Li, Xue Jiang, Yongwei Wang, Cong Zhang, Haiqing Yin, Xuanhui Qu

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Abstract

Microstructure plays a crucial role in predicting the properties of parts by additive manufacturing. Fluid flow and temperature gradient are always recognized as key factors influencing the final microstructure. However, the effects of flow field were often ignored during microstructure simulation inside the molten pool. In this study, the Marangoni flow is firstly calculated using the finite element method. Fluid flow increases the temperature gradient and the cooling rate at the solid front. Subsequently, the temperature field and flow field are input to phase-field model to simulate the microstructure inside the molten pool. This integrated model is then applied to study the solidification behavior of IN718 alloy during additive manufacturing. The microstructure evolutions are analyzed in detail under different processing parameters. The simulation results demonstrate that the Marangoni flow has great effects on both molten pool and solidification microstructure. The integrated model developed in this work can predict the molten pool and solidification microstructure more accurately by combining the thermal, flow and microstructure models together.

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研究熔池和微观结构演变的马兰戈尼效应的综合多尺度模型

显微组织对增材制造中零件性能的预测起着至关重要的作用。流体流动和温度梯度一直被认为是影响最终微观结构的关键因素。然而，在模拟熔池内部微观结构时，往往忽略了流场的影响。本文首先采用有限元法对马兰戈尼流进行了计算。流体的流动增加了固体锋面的温度梯度和冷却速率。然后，将温度场和流场输入相场模型，模拟熔池内部的微观结构。将该综合模型应用于IN718合金增材制造过程中的凝固行为研究。详细分析了不同工艺参数下的微观组织演变。模拟结果表明，马兰戈尼流动对熔池和凝固组织都有较大的影响。本文建立的综合模型将热、流动和微观组织模型结合在一起，可以更准确地预测熔池和凝固组织。

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来源期刊

Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering

CiteScore

5.30

自引率

9.10%

发文量

审稿时长

39 days

期刊介绍： The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.