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Engineering Science in
Additive Manufacturing Multi-material additive manufacturing of metals

modeling software or open-source codes have been
developed for single-material processes; thus, simulation
platforms specifically tailored for MMAM remain
underdeveloped, unvalidated, and unqualified. In this
section, we review existing literature on the computational
analysis of bimetallic structures and processes, with a
focus on developments toward MMAM. Specifically, the
remainder of this section discusses research conducted
on simulating melt pool dynamics, elemental intermixing,
phase diagrams, and grain growth during solidification.
Although extensive studies have been conducted on
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Figure 14. Thermal diffusivity of IN718/CuA bimetallic structure, single-material melting and solidification, Sorkin et al.
including measured values for monolithic IN718 and GRCop-84, presented the first published attempt at simulating MM
alongside experimental and theoretical thermal diffusivity values for the melting using the open-source molecular dynamics software
bimetallic composition. The comparison illustrates the thermal diffusivity
behavior of the bimetallic structure relative to its constituent materials Large-scale Atomic/Modular Massively Parallel Simulator
across a temperature range of 50°C to 300°C. (LAMMPS), albeit with numerous simplifications. However,
the model’s assumptions neglected several key physical
spreading dynamics, and particle–laser interactions during processes involved in LPBF, including surface tension forces,
melting. These simulations, often conducted using the multiphase flow, and molten pool formation. Moreover,
discrete element method (DEM), help optimize powder no experimental validation was provided to support the
shape or size distribution to achieve ideal powder flow simulation results. Beyond melt pool dynamics, powder
characteristics, thereby improving layering and reducing deposition plays a significant role in MM applications,
mesoscale defects. The CFD-VOF model typically includes influencing interfacial properties based on packing,
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a laser heat source and a coaxial nozzle with a single powder design, and deposition strategies. Gu et al. investigated
feeder aligned with the laser path. This model captures key various deposition patterns involving multiple materials
phenomena during LDED, such as powder and gas flow by incorporating particle size distribution data obtained
dynamics, laser–powder interactions, attenuation effects, from a particle size analyzer and simulating it employing
and melt pool behavior. the DEM (EDEM v2019). The studied deposition patterns
included evenly mixed, bimetallic separation, interlock
5.1. Microstructural computational analysis (phase deposition, and FGM. Further discussion on the influence
transformation, melt pool formation, and alloy of these deposition patterns on melt pool formation is
mixing) provided later in this section.
Simulating and predicting the microstructural evolution The characteristics of the melt pool, including
at the interface of bimetallic structures fabricated using temperature gradients, thermodynamics, fluid dynamics,
MMAM is critical to optimizing their performance in and laser–matter interactions, have been thoroughly
industrial applications. Microstructural simulation and studied in LPBF and LDED processes. Sun et al. applied
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modeling serve as powerful tools to bridge the gap in a DEM approach using ABAQUS FLUENT for multiphase
understanding the links between processing conditions mesoscale modeling of an MM powder bed composed of
and final material properties. These tools enable the capture IN718/Cu10Sn (Figure 15A). This study simulated several
of solidification behavior, phase transformations, grain powder configurations within a single layer (aligned
morphology, and elemental diffusion across dissimilar parallel or perpendicular to the laser scan path, and pre-
materials. In bimetallic structures with significant mixed by wt.%). The simulations focused on melt pool
differences in thermal properties, computational analysis morphology and thermal behavior during single-track
is essential for evaluating the nature of bonding, transition laser scans. To simplify the model, the two components
zone development, and the formation of undesired IMCs, were treated as mutually soluble, enabling microlevel
porosity, or other defects. Various techniques—such as mixing. When comparing tracks composed of mixed
the Monte Carlo method, CALPHAD, Lattice Boltzmann, versus unmixed dissimilar powders, melt pools with a high
Cline–Anthony model (for melt pool geometry), and content of IN718 exhibited higher peak temperatures due
smoothed particle hydrodynamics (SPH)–are employed to their thermo-physical properties. In the unmixed case,
to simulate grain growth, phase diagrams, diffusion at the melt pool size and temperature transitioned gradually
interfaces, and microscale kinetics. Most solidification as the laser moved from one material to the other.

Volume 1 Issue 2 (2025) 25 doi: 10.36922/ESAM025180010