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

was optimized by strategically selecting the material underrepresented in numerical modeling for MMAM. These
combination configuration. The absence of standardized phenomena are especially critical at material interfaces,
guidelines regarding material deposition configuration where differing thermal properties of dissimilar materials
in MMAM highlights a pressing need to establish testing have a major influence. Beyond advancing particle-based
protocols that account for interfacial bonding strength. simulation approaches, the simulation of multi-track and
To further assess interfacial strength, testing standards multi-layer builds is inevitable for linking microscale
must be adapted to evaluate the mechanical performance behavior to part-scale mechanical performance. Modeling
of MM specimens in multiple dimensions. A notable gap of single- and multi-track and multi-layer in single-material
exists in the literature concerning MMAM structures with AM has been crucial for understanding defect formation
and predicting mechanical properties. This approach needs
complex radial interfaces in the horizontal plane while
maintaining vertical consistency. to be expanded to encompass MMAM. Recent work by
Küng et al. has begun to address this, employing a two-
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With the site-specific material deposition enabled dimensional PBF model using the Lattice–Boltzmann-based
by AM, traditional strain measurement techniques simulation (a class of CFD simulation), as well as the DEM-
may be insufficient. In cases where dissimilar materials SPH method. To further accelerate the understanding of
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are deposited in non-conventional orientations (e.g., the multi-track and multi-layer phenomena, the community
horizontally or vertically), data from extensometers, is encouraged to investigate the integration of DEM-
crosshead displacement, or strain gauges may not SPH/optimal transportation meshfree—a particle-based
accurately capture local strain behavior within the method formulated for simulating solid and fluid flows—to
specimen. In this context, digital image correlation (DIC) address part-scale size challenges. In single-material AM,
emerges as a valuable tool for strain measurement. DIC SPH has experienced extensive developments due to its
enables the acquisition of full-field local strain data across algorithmic maturity and established track record, which
the specimen surface, allowing researchers to detect could serve as a foundation for MMAM. Insights from
strain localization, necking, and crack initiation. Beyond multi-track and multi-layer approaches can also inform
these advantages, DIC also provides critical information FEA simulations, helping to advance the understanding of
about interface performance and enables the generation MMAM’s mechanical behavior. However, as discussed in
of contour maps across the entire specimen, supporting Section 5, existing FEA simulations for MMAM often rely
detailed visualization and quantification of strain behavior. on assumptions that compromise accuracy when compared
to experimental data. These assumptions stated throughout
6.4. Thermo-mechanical modelling (part-scale) the studies will not provide accurate results when compared
Even though a substantial number of simulations have been to the experimental data. Assumptions and simplifications
conducted on MMAM, the field remains less extensive such as a smooth, roughness-free surface, a defect-free or
and well-developed compared to single-material AM. This crack-free interface, and a well-bonded interface will yield
research gap stems from the developmental and fundamental inaccurate results in MMAM, due to their influence on
complexities of MMAM. MMAM introduces additional mechanical behavior. One way to address these limitations
challenges in parameter selection to achieve ideal melting, is to integrate X-ray micro-computed tomography imaging
mixing, and solidification conditions. In single-material data as inputs into modeling workflows, enabling more
AM, selecting parameters often involves optimizing laser accurate mechanical behavior predictions. In 2024,
power and scan speed for a given material; however, this Auenhammer et al. proposed an approach to overcome
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approach cannot be applied to MMAM, as it would entail the image-based numerical modeling for carbon fiber
extensive experimental costs and time. To address this, a using an open-source Python script. While the method
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process simulation framework must be developed to identify has some drawbacks, it offers a promising future direction
reasonable process parameters, mitigate significant defects, for extending image-based numerical modeling to both
and achieve desired cooling rates and microstructures. single- and MMAM.
Such a framework would lower the experimental burden
required for case-by-case validation of simulation results. 6.5. Future direction
The advancements in high-fidelity PBF simulations and the MM-AM has emerged as a transformative approach in
capability of CFD to deliver detailed, high-resolution results engineering, enabling the integration of distinct metals
need to be extended to MMAM. The importance of particle- within a single component to harness their complementary
based simulations is attributed to the core understanding properties. However, when it comes to discrete metal
of the physical phenomena, such as evaporation, recoil transitions, such as joining high-conductivity Cu to
pressure, and surface tension, factors that are currently high-strength steel or combining corrosion-resistant

Volume 1 Issue 2 (2025) 32 doi: 10.36922/ESAM025180010