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

6.2. In-process monitoring When combined with mesoscale DEM modeling, these

In-situ process monitoring includes multiple techniques techniques can enhance the prediction of melt pool
applicable to all MMAM processes, allowing for real-time solidification behavior.
observation and analysis of the manufacturing process. Thermal imaging uses IR cameras to capture emitted
These methods are essential for assessing the build quality, radiation during the AM process, allowing visualization
particularly at the MM interface. In-situ monitoring data of temperature gradients, hotspots, and cooling rates.
can also be used to calibrate and inform MM simulations This data is used to identify proper fusion between
across various length scales, depending on the monitoring adjacent layers and detect anomalies with distinct thermal
technique used. signatures, such as incomplete melting. Thermal maps
generated through this method can also be used to calibrate
In-situ monitoring techniques can be broadly
categorized into acoustic monitoring, optical imaging, and and validate macro-scale process models and simulations,
enhancing the understanding of thermally induced
thermal imaging, and are commonly paired with machine distortions and residual stresses. In MMAM, metal
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learning algorithms for real-time defect prediction.
matrices often exhibit non-uniform heating and cooling
Acoustic monitoring utilizes sound waves detected behavior due to differences in material solidification and
during fabrication as predictive tools to identify internal thermal properties. Thermal imaging can thus serve as an
defects. Acoustic signals generated during processing input-control mechanism in real-time monitoring systems
can be correlated with internal irregularities, enabling to maintain uniform thermal profiles—such as heating/
the development of AE monitoring systems capable of cooling rates or thermal expansion/contraction rates—
classifying AE wave patterns associated with specific across the build plate, even in the presence of bimaterial
processing conditions, such as conduction mode, boundaries. 233
lack-of-fusion pores, and keyhole pore formation.
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Compared to other in-situ methods, this approach is 6.3. Standardization of MM testing
relatively straightforward and particularly useful for Mechanical property characterization tests conducted
detecting internal defects that are otherwise difficult to on monolithic materials fabricated using AM processes
identify. Acoustic monitoring is commonly combined typically follow standard testing procedures established
with complementary optical techniques (e.g., high-speed for conventionally manufactured specimens. While this
cameras, IR sensors, photodiodes) to improve detection approach is suitable for monolithic materials, it is not fully
reliability. One limitation of AE monitoring as a standalone applicable to MMAM due to the unique characteristics
method is the challenge of isolating AE sound waves of the MM interface between dissimilar materials. The
produced by laser-material interactions from ambient noise interfacial bonding between two dissimilar materials
generated by the machine or environment. Machine plays a significant role in determining the failure point
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learning algorithms are frequently used to deconvolute and overall mechanical performance of the specimen.
these signals, helping to identify and differentiate AE wave Similar to the influence of build orientation in AM, the
patterns associated with defective conditions. 221,222 This material deposition configuration significantly affects
approach has been well studied in the context of single- the mechanical performance of MMAM components. To
material AM fabrication. 220,223-228 the authors’ knowledge, most experimental studies have
not thoroughly investigated deposition configurations
Build-plate imaging is another widely used in-situ
technique that monitors the fabrication process on a layer- to identify optimal strategies for enhancing mechanical
performance. For example, Chen et al. manufactured
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by-layer basis. High-speed cameras provide real-time SS316L/CuSn10 structures using LPBF with SS316L as the
feedback on surface quality, layer deposition, and process base material and conducted mechanical testing, such as
anomalies such as spatter. 229,230
microhardness, tensile, and flexural strength tests. Their
Radiographic imaging, including X-ray and computed tensile testing results indicated that horizontally combined
tomography, offers cross-sectional views of the process, specimens exhibited greater elongation than vertically
revealing internal structures, voids, and defects. In combined ones, although the UTS was similar in both
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MMAM, these methods can differentiate most material configurations. Similarly, Dash and Bandyopadhyay
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pairs based on grayscale contrast related to material fabricated vertically and radially combined SS316L/17-
density. Cross-sectional imaging of the melt pool at metal 4PH specimens using MM-LDED and observed that
interfaces provides fundamental insights into melt pool radially combined specimens exhibited higher compressive
formation mechanisms, supplementing post-process strength than both vertically combined specimens and
techniques typically used to analyze heat-affected zones. wrought SS316L. In both cases, mechanical performance

Volume 1 Issue 2 (2025) 31 doi: 10.36922/ESAM025180010