Theoretical model for predicting microstructural evolution in superalloys under directed energy deposition (DED) Processes
1 Department of Mechanical Engineering, McNeese State University, Louisiana, USA.
2 TotalEnergies Exploration & Production Nigeria Limited.
3 Shell Nigeria Gas (SEN/ SNG), Nigeria.
4 Shell Petroleum Development Company, Lagos Nigeria.
5 Shell Energy Nigeria PLC.
Review Article
Magna Scientia Advanced Research and Reviews, 2022, 05(01), 076–089
Article DOI: 10.30574/msarr.2022.5.1.0040
Publication history:
Received on 10 April 2022; revised on 11 June 2022; accepted on 13 June 2022
Abstract:
Directed Energy Deposition (DED) processes have emerged as a pivotal additive manufacturing technique for fabricating high-performance components using superalloys. The ability to predict microstructural evolution in these alloys during DED is critical for ensuring desired mechanical properties and structural integrity. This study presents a theoretical model for predicting the microstructural evolution of superalloys under the complex thermal and mechanical conditions inherent in DED processes. The proposed model integrates thermodynamic principles, kinetic simulations, and phase-field modeling to capture the interactions between thermal gradients, solidification dynamics, and phase transformations. Key variables include deposition parameters, cooling rates, and alloy composition, which collectively influence grain growth, dendritic structures, and precipitation behavior. By incorporating computational thermodynamics, the model enables real-time predictions of phase stability and morphology changes during deposition and solidification. Finite element analysis (FEA) is utilized to simulate the thermal cycles and stress distributions that drive microstructural changes. Additionally, the model accounts for the effects of multiple thermal cycles, such as reheating and remelting, which significantly impact grain refinement and residual stresses. Machine learning techniques are employed to refine predictions by analyzing large datasets generated from experimental and simulated results. The model is validated through experimental studies on nickel-based superalloys using advanced characterization techniques, including electron microscopy and X-ray diffraction. Results demonstrate the model's capability to accurately predict grain structure, phase distribution, and mechanical property variations, thereby providing insights into optimizing process parameters for improved material performance. This study establishes a foundational framework for understanding and controlling microstructural evolution in superalloys during DED processes. The theoretical model offers significant potential for enhancing the reliability and efficiency of additive manufacturing in industries such as aerospace, energy, and automotive, where superalloys are extensively used.
Keywords:
Directed Energy Deposition; Superalloys; Microstructural Evolution; Phase-Field Modeling; Thermal Cycles; Solidification Dynamics; Finite Element Analysis; Machine Learning
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