Exploring new frontiers in additive manufacturing: advancing nickel-based superalloys with multi-physics modelling 

Additive manufacturing (AM) has revolutionised the way we produce complex parts, especially in industries such as aerospace, automotive, and energy, where the demand for high-performance materials is crucial.  
Exploring new frontiers in additive manufacturing: advancing nickel-based superalloys with multi-physics modelling 
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Exploring new frontiers in additive manufacturing: advancing nickel-based superalloys with multiphysics modelling 

By Dr Chinnapat Panwisawas, Queen Mary University of London 

One of the most widely used methods in AM is Laser Powder Bed Fusion (LPBF), a process where a laser selectively melts layers of metallic powder to create a three-dimensional part. However, despite its advantages in precision and customisation, LPBF faces critical challenges when it comes to nickel-based superalloys.

These superalloys, known for their exceptional mechanical properties and resistance to extreme environments, are multi-component materials consisting of 10 to 15 different elements. Their versatility makes them essential for high-temperature applications, particularly in gas turbines and jet engines [1]. However, during the LPBF process, selective vapourisation of certain alloying elements occurs, leading to undesired changes in the chemical composition of the final part. This poses significant problems, including reduced mechanical performance and corrosion resistance, which can severely impact the reliability and longevity of these components. 

To address this issue, our research team at Queen Mary University of London, in collaboration with Pennsylvania State University, Iowa State University (USA), and Shimane University (Japan), developed a novel multiphysics modelling framework. Our findings were recently published in npj Computational Materials by Spinger Nature. 

The challenge: controlling composition in additive manufacturing 

In traditional manufacturing processes, the chemical composition of an alloy can be tightly controlled. However, in LPBF, the high-intensity laser used to melt the metal powder can lead to the selective evaporation of alloying elements such as chromium, cobalt, and aluminum. As a result, the composition of the part can deviate from that of the original powder feedstock. Even slight variations in the concentration of these elements can degrade the alloy's performance, rendering the part unsuitable for demanding applications. 

Compounding the issue, the layer-by-layer nature of LPBF means that each layer is subject to remelting during the deposition of subsequent layers, which further exacerbates the challenge of maintaining a uniform chemical composition. These factors highlight the need for a more sophisticated understanding of the LPBF process to achieve greater control over the final material properties. 

Our approach: A multiphysics modelling framework 

To tackle this challenge, we developed a multiphysics model that integrates several key factors influencing the LPBF process: 

  1. Heat and fluid flow simulations: These simulations allow us to track the temperature distribution and material flow during the melting and solidification of the powder. Understanding how heat is distributed across the build is essential for predicting which areas are prone to vapourisation and remelting. 

  1. Thermodynamic calculations: By modelling the thermodynamics of the alloy system, we can predict the phase changes that occur as the material cools and solidifies. This helps us understand how different alloying elements behave under the conditions of the LPBF process. 

  1. Evaporation modelling: This critical component of our framework estimates the evaporative flux of each alloying element, allowing us to quantify the extent of composition change. By modelling how much of each element is lost during evaporation, we can predict the final chemical composition of the part. 

The novelty of our approach lies in combining these simulations into a single, cohesive framework that provides a more detailed understanding of how the LPBF process affects the chemical composition of nickel-based superalloys [2]. Importantly, we validated our model through experimental trials, confirming its accuracy in predicting composition changes. 

Key insights and applications 

Our study produced several important findings that can be used to optimise the LPBF process for nickel-based superalloys: 

  1. Spatial variations in evaporative flux: We found that the evaporation of alloying elements is not uniform across the build area. Certain regions of the part experience higher rates of evaporation, leading to localised composition changes. Understanding these spatial variations is crucial for optimising process parameters to minimise composition change. 

  1. Effect of remelting: Our model also highlighted the significant impact of remelting on the final composition of the part. Each subsequent layer of material deposited during the LPBF process can remelt the underlying layer, leading to further loss of volatile elements. By quantifying this effect, we can make informed adjustments to process settings such as laser power, scanning speed, and powder layer thickness to mitigate these losses. 

  1. Relative vulnerabilities of superalloys: Our study compared the composition stability of different nickel-based superalloys, identifying which compositions are more susceptible to vapourisation-induced changes. This information is invaluable for selecting the right material for specific applications, especially those that demand high chemical uniformity. 

The insights gained from our research can be directly applied to improving the quality and performance of parts produced by LPBF. By optimising the process parameters based on our model’s predictions, manufacturers can produce components that maintain their desired chemical composition and, by extension, their mechanical properties and corrosion resistance. This has far-reaching implications for industries that rely on nickel-based superalloys, including aerospace, energy, and medical sectors. 

Looking ahead: collaborations and future research 

Our collaboration with institutions in the USA and Japan was instrumental in advancing this research. The combination of expertise from Pennsylvania State University, Iowa State University, and Shimane University allowed us to develop a comprehensive approach to a complex problem. As we continue to explore new ways to optimise the LPBF process, we look forward to further international collaborations that will push the boundaries of additive manufacturing technology. 

In the future, we aim to validate not only the temperature field but also the chemical signal which includes volatile elements in real time to assure that the composition and part integrity of the additive manufactured structure are fully controllable for further scaling up and technology take-up.  

Additive manufacturing offers immense potential for producing high-performance components with complex geometries. However, controlling the chemical composition of materials during the LPBF process remains a critical challenge, particularly for nickel-based superalloys. Our research provides a novel solution by integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modelling into a single framework. By better understanding the factors that influence composition change, we can optimise the LPBF process to produce components that meet the exacting standards of industries that rely on nickel-based superalloys. 

We’re excited to see how these findings can be applied to real-world manufacturing processes and look forward to continuing our work in this rapidly evolving field. 

References:

[1] Panwisawas, C.,  Tang, Y.T. & Reed, R.C.  Metal 3D printing as a disruptive technology for superalloys. Nature Communications 11, 2327 (2020). DOI: 10.1038/s41467-020-16188-7

[2] Mukherjee, T.,  Shinjo, J., DebRoy, T. & Panwisawas, C. Integrated modelling to control vaporisation-induced composition change during additive manufacturing of nickel-base superalloys. npj Computational Materials 10, 230 (2024). DOI: 10.1038/s41524-024-01418-z

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Metals and Alloys
Physical Sciences > Chemistry > Materials Chemistry > Metals and Alloys
Computational Materials Science
Physical Sciences > Materials Science > Computational Materials Science
Materials Engineering
Technology and Engineering > Mechanical Engineering > Materials Engineering

Related Collections

With collections, you can get published faster and increase your visibility.

Machine Learning Interatomic Potentials in Computational Materials

Publishing Model: Open Access

Deadline: Jun 06, 2025

Self-Driving Laboratories for Chemistry and Materials Science

Publishing Model: Open Access

Deadline: Jul 08, 2025