Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Abstract

Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed.

3D Multi-Track and Multi-Layer Epitaxy Grain Growth Simulations of Selective Laser Melting

“…Overall, the model presented in this study provides useful insights into the effects of the SLM process parameters on the final microstructure of the printed part and therefore serves as a useful tool in tuning the SLM parameters in such a way as to produce SLM parts with the required microstructural properties and mechanical behavior. In the future, researchers can use a similar approach as introduced in [ 53 , 54 ] for an in situ observation through Machine Learning for microstructure modeling, which can help to control the final properties for SLM manufactured parts.…”

The thermodynamics of non-equilibrium interfaces during phase transformations in concentrated multicomponent alloys

“…The final properties of components made via AM are controlled by the solidification microstructures that form during processing. Because the solidification velocities in powder-bed fusion AM processes can easily approach ∼ 1 m • s −1 [1,2,3], quantitative predictions of the microstructures formed during AM require models of rapid solidification that incorporate the effects of an interface that departs from local equilibrium [4,5]. This is further supported by observations of non-equilibrium effects such as solute trapping via experiment [6,7], molecular dynamics simulations [8,9], phase-field simulations [10,11], and phase-field-crystal simulations [12].…”

SAMPLE3D: A Versatile Numerical Tool for Investigating Texture and Grain Structure of Materials Processed by PBF Processes

“…In contrast, numerical simulation has emerged as an effective alternative method. Recent reviews [5,6] have highlighted the growing role of numerical simulations in studying microstructure evolution during PBF. Numerical simulations can not only be used to aid in microstructure customization, but also enable an in-depth understanding of microstructural evolution during processing.…”