Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-09-30 DOI:10.1038/s41524-024-01418-z
Tuhin Mukherjee, Junji Shinjo, Tarasankar DebRoy, Chinnapat Panwisawas
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Abstract

A critical issue in laser powder bed fusion (LPBF) additive manufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of parts. The process also alters the part’s chemical composition compared to the feedstock. Here we present a novel multi-physics modeling framework, integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modeling to estimate and control the composition change during LPBF of nickel-based superalloys. Experimental validation confirms the accuracy of our model. Moreover, we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF process. Spatial variations in evaporative flux and compositions for each element were determined, providing valuable insights into the LPBF process and product attributes. The results of this study can be used to optimize the LPBF process parameters such as laser power, scanning speed, and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.

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在镍基超合金增材制造过程中控制汽化诱导成分变化的综合建模
激光粉末床熔融(LPBF)快速成型制造中的一个关键问题是合金元素的选择性汽化,这会导致零件的机械性能和耐腐蚀性变差。与原料相比,该工艺还会改变零件的化学成分。在此,我们提出了一种新颖的多物理场建模框架,将热流和流体流动模拟、热力学计算和蒸发建模整合在一起,以估计和控制镍基超合金 LPBF 过程中的成分变化。实验验证证实了我们模型的准确性。此外,我们还定量分析了不同镍基超耐热合金对成分变化的相对脆弱性,并研究了 LPBF 过程中逐层沉积导致的重熔效应。我们确定了蒸发通量和每种元素成分的空间变化,为 LPBF 工艺和产品属性提供了宝贵的见解。这项研究的结果可用于优化 LPBF 工艺参数,如激光功率、扫描速度和粉末层厚度,以确保生产出具有所需化学成分的高质量元件。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction Setting standards for data driven materials science High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys Machine-learned coarse-grained potentials for particles with anisotropic shapes and interactions
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