Melt Pool Size Research Articles

Physics-guided spatiotemporal approach for melt pool size prediction in laser powder bed fusion

ABSTRACT This study first proposes a physics-guided spatiotemporal graph. The melt pool temporal feature nodes and spatial feature nodes are extracted from melt pool images based on spatiotemporal correlations. These feature points are treated as graph nodes, and the edge weights in the graph are calculated based on an analytical heat transfer model. It supplements the physical information of accumulated heat. Based on this, the graph convolutional network (GCN) is used to extract spatiotemporal information of heat transfer, and the long short-term memory (LSTM) is used to capture the evolution patterns of the melt pool. Thus, a melt pool spatiotemporal feature extraction model is constructed for melt pool size prediction. The model achieves prediction errors within 12% for the melt pool area and 8.5% for the melt pool perimeter. Subsequently, interpretability analysis indicates that the physics-guided approach enhances the model's learning ability for the cumulative contribution of heat from neighboring tracks.

On morphology and roughness of upskin surfaces in laser powder-bed fusion additive manufacturing – Contouring strategy effects

The surface quality of inclined parts made by Laser powder-bed fusion (L-PBF) additive manufacturing is inferior to those made by conventional formative and subtractive manufacturing techniques. The inherited step edges formed during such layer-by-layer fabrications and partially melted or non-melted particles attached to the newly formed surface are the major causes of poor surface finish of L-PBF parts. This study is a part of a research framework, in which the effects of different contouring strategies and scan parameters on the surface morphology of inclined parts are investigated by both experimental and numerical methods. In particular, the effect of two different contour scan strategies, namely pre-contouring and post-contouring, on the upskin surface roughness of 30⁰ inclined parts is analyzed. The inclined specimens were fabricated with Ti6Al4V powder using an EOS M270 system and the upskin surfaces were measured by a white light interferometer. In addition, a multi-track multi-layer numerical approach using coupled discrete element method and thermo-fluid simulation was also performed to understand the inclined surface formation in L-PBF.The findings show that the pre-contouring strategy with high linear energy density (LED) (0.4 J/mm) results in average surface roughness (Sa) as low as 7.43 µm, whereas the lowest LED (0.05 J/mm) resulted in the highest Sa of 33.74 µm. Larger melt pool dimensions were obtained in the high LED pre-contouring simulations, which indicates a better material fusion resulting in a good surface finish. The numerical simulation showed that the upskin surface roughness is also affected by the raster scan, if the raster scanning LED is higher than that of pre-contour scans. For the post-contouring strategy studied, higher and lower LEDs were used in inner and outer contour scanning, respectively. It is indicated that the inner contour scanning with a high LED reduces the surface roughness because of a larger melt pool size, irrespective of outer contour scanning. The lowest Sa obtained was 8.24 µm for the highest LED inner contour (0.05 J/mm) case and the highest Sa was 19.95 µm, for the parts made with the lowest LED inner contour scan (0.075 J/mm) strategy. The coupled discrete-element and thermo-fluid-based simulations offer insights into the sloped surface formation and the results supported the experimental findings in qualitative manner.

A novel mesoscale transitional approach for capturing localized effects in laser powder bed fusion simulations

This paper introduces a new mesoscale approach for considering local fluctuations of powder bed characteristics in laser powder bed fusion (PBF-LB/M) simulation, bridging the gap between computational fluid dynamics (CFD) and homogenized mesoscale models. Gusarov and Sih's models for laser heat input and powder bed thermal conductivity were applied to local powder bed elements (PBE) to capture characteristics in the powder bed. Discrete element method (DEM) simulations of the recoating process generated a representative powder bed and determined local porosity values. These values informed local optical thicknesses and thermal conductivities, providing insights into heat transfer in PBF-LB/M. Single-track test simulations showed melt pool sizes matched best with homogenized parameters. The PBE-based approach resulted in smaller melt pools and revealed asymmetries in heat distribution due to varying powder bed characteristics, highlighting the need for accurate powder bed modeling. This mesoscale approach offers an efficient, time-saving alternative to CFD simulations.

Very high cycle fatigue characteristics of laser beam powder bed fused AlSi10Mg: A systematic evaluation of part geometry

This study explores the influence of geometry and part size on defect distribution, melt pool size, and mechanical characteristics in laser beam powder bed fused (LB-PBF) AlSi10Mg. Five distinct geometries—hourglass, small rod, small block, large block, and large rod—were fabricated under identical process parameters. Fully reversed ultrasonic fatigue testing, operating at a frequency of 20 kHz, was conducted to assess the very high cycle fatigue properties. The findings indicated that part geometry had a major impact on the fatigue properties of the material in the very high cycle fatigue regime. Specimens machined from large rods and large blocks had the lowest porosity and highest fatigue resistance. Microstructural analysis indicated that hourglass, small rod, and small block specimens had shallower melt pools and overlap depths compared to other geometries. This observation suggests a higher cooling rate in specimens with smaller cross-sectional areas, leading to the increased presence of entrapped gas pores and a lack of fusion defects. Understanding the relationship between part geometry and fatigue properties in LB-PBF components offers insights for optimizing design and manufacturing processes in additive manufacturing applications.

In situ x-ray imaging to understand subsurface behavior during continuous wave laser drilling

A limited understanding regarding the underlying dynamics and mechanisms of material removal during continuous wave laser drilling has presented significant challenges in achieving precision and process control. To address this, we employed high-fidelity, in situ synchrotron x-ray imaging to reveal previously unknown material behaviors during continuous wave laser drilling with power modulation. Our findings highlight that high-aspect ratio drill holes are achieved when the laser modulation frequency falls within the range of 8–12 kHz, provided that the laser average power and modulation amplitude levels meet the specified limits. Under these conditions, we identified a material removal mechanism driven by incremental accumulation of recoil pressure that gradually pushes material upward from deep within the substrate to the surface. This mechanism manifested as a low-frequency fluctuation in the vapor depression depth, resulting in periodic instances of material ejection. Furthermore, our study underscores that rapid expansion of the melt pool and the widening of the drill hole opening can impede effective material removal by redirecting energy from material ejection to increasing the melt pool size. This investigation contributes essential insights into the subsurface dynamics involved in the drilling of high-aspect ratio holes, furthering our fundamental understanding of this intricate process.

Data-driven prediction of future melt pool from built parts during metal additive manufacturing

Smart manufacturing in metal additive manufacturing (MAM) relies on real-time process optimization through the prediction of unbuilt parts using data from built parts. Melt pool dynamics are intimately associated with the development of various microstructures during the MAM. In this paper, we demonstrate the idea by establishing a machine learning (ML) model to predict the melt pool of future parts, based on the experimental data from the National Institute of Standards and Technology (NIST) where 80 % is used for the training (built parts) and 20 % for testing (future parts). The ML model integrates both data denoising and predictive modeling. First, a convolutional neural network (CNN) is used to process a large dataset of raw melt pool images, enabling the automatic removal of noises (e.g., splash and plume) and thereof extraction of high-quality melt pool data. Following that, a novel data-driven melt pool model based on multi-layer perceptron (MLP) is trained by incorporating raw, long scanning history as input features, which best accounts for the effects of printing history (e.g., intertrack heating) on melt pool development. It takes complete advantage of MLP in handling high-dimensional regression problems in conjunction with a large dataset. Upon testing under various manufacturing conditions, the average relative error magnitude (AREM) of predicting melt pool size drops to 2.8 %, compared to 14.8 % of the prior art – the Neighboring Effect Modeling Method model (NBEM). This research thus represents a significant step towards reliable melt-pool-guided AM process optimization for smart manufacturing, enabled by advanced, flexible, and maximum use of ML techniques.

Numerical simulation of single-pass selective laser melting of Mg-Y-Sm-Zn-Zr alloy

A three-dimensional finite volume method (FVM) thermal-fluid coupling model was developed to simulate the temperature field distribution and dimensions of the melt pool in selective laser melting (SLM) of Mg-Y-Sm-Zn-Zr alloy powder. This model aims to accurately determine the temperature field and surface morphology of the melt pool by considering the laser's impact on powder particles, free surface dynamics, convection, and multiphase heat transfer. The study explores how laser power and scanning speed influence temperature distribution, melt pool size, and flow dynamics. The findings show that increasing laser power or reducing scanning speed consistently affects the melt pool size, peak temperature, and cooling rate. During the evaporation of the alloy in the melt pool, fluid flow is primarily governed by recoil pressure and Marangoni convection. Notably, increasing the laser power from 40 W to 100 W and the scanning speed at 0.3 m/s enhances the depth of the melt pool indentation from 13.86 µm to 23.35 µm. Furthermore, the indentation depth correlates positively with the laser line energy density.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (Ms), and coercivity (Hc)) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, Ms, and Hc. The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas Ms and Hc displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high Ms was achievable through low v, while low Hc resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for Ms, a clear association was established between Ms and v, with lower v values corresponding to higher Ms values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

Understanding the formation of laser-induced melt pools with both wire and powder feeding in directed energy deposition

Wire-powder fed directed energy deposition (WP-DED) has been applied to produce high-performance metal parts. Compared to DED with the feed of a single filler (either powder or wire), simultaneous WP-DED exhibits more complex interactions between fillers and laser-induced melt pools, such as additional powder collisions by wire, different filler properties, intricated heat transfer paths, etc. At present, there is little mechanistic understanding of the interactions among wire, powder particles, and laser-generated melt pools in the WP-DED. In this work, we discovered the filler-melt pool interactions in a coaxial wire-powder fed DED (CWP-DED) process by in situ experimental characterizations. In particular, we investigated the wire-influenced particle dynamics before entering the melt pool. When in-flight particles interact with the melt pool, the temporal-varied melt pool geometry as well as melt flow dynamics were characterized. The bubble formation and evolution in CWP-DED were also revealed. We found that the extruded wire would affect the trajectory of in-flight particles and reduce their velocity before the entry of melt pool due to the inelastic collision. The melt pool size decreased with powder feed rate; however, the powder feed rate had limited influences on the melt pool oscillation. Four modes of fluid flow were observed in the melt pool, including backward flow, upwards flow to the wire-melt pool interface, rebounded flow, and clockwise flow. At a high powder feed rate, more gas-induced bubbles occurred along the melt pool boundary and wire-melt pool interfaces. This study provides a fundamental understanding of filler-melt pool interactions during the CWP-DED process to advance the development of next-generation, fast-rate additive manufacturing technology.

Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges

The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.

Relating laser powder bed fusion process parameters to (micro)structure and to soft magnetic behaviour in a Fe-based bulk metallic glass

This work aims to establish fundamental processing-(micro)structure-property links in a commercial Fe-based Kuamet6B2 bulk metallic glass (BMG) processed by laser powder bed fusion (LPBF). With that purpose, amorphous powders were processed using a pulsed-wave system and a simple meander strategy. The laser power, the scan speed, and the hatch distance were varied over wide intervals within the conduction regime. The processability window leading to samples with good dimensional accuracy and mechanical stability was determined. Within this window, the manufactured samples were crystalline/amorphous composites and the crystalline regions were formed by equiaxed ultrafine and nanograins with random orientations. Processing parameters yielding the densest prints caused severe crystallization while, conversely, parameter sets allowing the material to retain a high amorphous fraction led to significant lack-of-fusion defects and residual cracking along directions perpendicular to crystalline/amorphous interfaces. Comparatively, for a fixed hatch distance, the scanning speed had a stronger effect than the laser power in the resulting amorphous fraction due to its stronger influence on the melt pool size and, in turn, on the corresponding HAZ volume. The saturation magnetization and the coercive field were inversely related to the amorphous fraction. This work allows to derive fundamental guidelines for the successful additive manufacturing of soft magnetic Fe-based bulk metallic glasses (BMGs) by laser powder bed fusion (LPBF).

Effect of process parameters on the roughness and tensile behavior of parts manufactured by the metals LPBF process

AbstractThe laser powder bed fusion process for metals (LPBF‐M) results in the development of stochastic surface features that significantly influence the interactions between parts and their surrounding environment, as well as their mechanical properties. The process parameters influence the surface quality, which is quantified by the surface roughness. Therefore, customizing the surface roughness during the build process can significantly contribute to obtaining ready‐to‐use parts, reducing the need for extensive surface posttreatments. This paper utilizes theoretical estimations of melt pool depth under iso‐linear energy density, iso‐power, and iso‐temperature manufacturing process parameter conditions. These estimations are then compared with experimental evaluations of surface roughness and tensile strength in upright‐built specimens to extract the trends in terms of the input energy versus roughness, and the input energy versus tensile behavior. The results show that iso‐energy values yield similar roughnesses due to the consistent expected melt pool depth. Moreover, an increase in melt pool depth generates higher surface roughness, while smaller melt pool dimensions result in improved roughness. Additionally, a comparison between the melt pool size and tensile test performance reveals a detrimental impact on the tensile strength for specimens estimated to have smaller melt pool depths.

Tailoring Microstructure and Mechanical Properties of Additively Manufactured Inconel 625 by Remelting Strategy in Laser Powder Bed Fusion

This study investigates the effect of laser power applied for a remelting scan in the laser powder bed fusion process on the formation of a bimodal microstructure and its impact on the mechanical properties of Ni-based Inconel 625 superalloy. Comparison of primary and remelting scans at similar surface energy densities revealed that the melt pools obtained in the remelting scan are smaller than in the primary scan. To achieve comparable remelted melt pool sizes, the 25 pct increase in energy is required. The shape and size of the remelted melt pools significantly affect the microstructure and material texture. The lower surface energy density in laser powder bed fusion favors the formation of a bimodal microstructure with large columnar grains and fine grain bands. Application of higher energy results in the formation of large columnar grains with Goss texture along build direction and separated by a large amount of low angle grain boundaries. Remelting scan also affects reduction of porosity and increasing of the area fraction of nanometric oxide inclusions. The study revealed that the samples subjected to a remelting laser scan and tensile tested along the direction of columnar grains exhibited higher ductility, which was associated with a slight decrease in the ultimate tensile strength compared to the samples that were not remelted. It was demonstrated that the remelting scan in the laser powder bed fusion process offers the possibility of improving the reliability of additively manufactured Inconel 625 superalloy by reducing porosity and tailoring its microstructure towards single-crystal-like, and thus improving the mechanical properties.Graphical

Thermal design of operating parameter for reliable AlSi7Mg selective laser melting

Gas turbines require high power density for applications like aircraft and future mobility. Increasing operating temperature, particularly turbine inlet temperature, boosts performance. However, conventional cooling methods limit this increase. Selective laser melting (SLM) offers a promising avenue for realizing advanced cooling configurations beyond conventional fabrication techniques. However, unexpected defects in the final product can occur, making pre-fabrication quality estimation difficult. This necessitates thermal analysis during SLM and design guidelines for suitable fabrication. This study investigates the thermal design guidelines for AlSi7Mg alloy using SLM for high-heat-flux applications. Simulations explored melt pool size dependence on operating parameters like laser power and scan speed. Comparisons with experimental results revealed diverse heat transfer modes under different operating conditions. Finally, a normalized enthalpy factor, integrating operating parameters like scan speed and laser power, was proposed for thermal design in SLM processes for high-heat-flux applications.

Gas-particle-heat dynamic coupling simulation in directed energy deposition

Powder flow can affect the temperature variations in directed energy deposition (DED). However, the direct coupling mechanism remains unknown. To solve this problem, the heat and mass transfer in additive manufacturing was simulated using dynamic coupling. The interactions between the multiphase flow and heat transfer were established. A comparison with experiment shows that the accuracy of the predictions of the numerical simulation regarding powder size distributions and temperature increases is higher than 95 %. The average temperature increase of the metal powders with different weight functions was highly consistent in the simulation process. As the powder size increases, the average temperature of the powder on the printing plane decreases. This was the reason for the formation of a deeper melt pool in the case of smaller particles in the experiment. The different curvatures between the particle surface and melt pool surface lead to a decreased energy absorption efficiency in DED. The relationship between the powder features and the melt pool size was studied. A decrease in the powder flow rate increased the temperature, leading to a larger melt depth.

A semantic segmentation algorithm for automated rapid melt pool identification from cross-sectional micrographs

Melt pool contours are an important empirical process measurement for metallic additive manufacturing. Melt pool boundaries indicate the maximal extent of the melting temperature contour. Melt pool boundaries characterize the effects of process parameters on melt pool size and variability and provide calibration and validation data for process simulations predicting thermal fields and microstructure. Serial cross sectioning of samples can produce numerous micrographs valuable for building robust melt pool contour datasets. However, the acquisition of contours from micrographs by hand is error prone and prohibitively time consuming for datasets large enough to capture variation within individual scan tracks and across a range of process conditions. In this work, we propose a semantic segmentation algorithm for automating melt pool contour acquisition from micrographs. The proposed algorithm is demonstrated on datasets from two different single scan track experiments conducted on a Powder Bed Fusion-Laser Beam platform using a bare Ti-6Al-4 V plate and an Inconel 718 plate with a 40 μm powder layer at different preheat temperatures. The algorithm was successfully trained and predicted the melt pool boundaries in the 300 image Ti-6Al-4 V dataset with 95% accuracy and an estimated reduction of 91% in processing time compared to data acquisition by hand. For the 1201 image Inconel 718 dataset, the algorithm achieved 89% accuracy and an estimated 99% time savings compared to hand measurements. The results for both datasets are demonstrated with a discussion about observed variability in the melt pool characteristics.

Theoretical-Numerical Investigation of a New Approach to Reconstruct the Temperature Field in PBF-LB/M Using Multispectral Process Monitoring

The monitoring of additive manufacturing processes such as powder bed fusion enables the detection of several process quantities important to the quality of the built part. In this context, radiation-based monitoring techniques have been used to obtain information about the melt pool and the general temperature distribution on the surface of the powder bed. High temporal and spatial resolution have been achieved at the cost of large storage requirements. This contribution aims to offer an alternative strategy of gaining information about the powder bed’s temperature field with sufficient resolution but with an economical amount of data. The investigated measurement setup uses a spectrometer to detect the spectral radiation intensities emitted by an area enclosing the melt pool and part of its surroundings. An analytical description of this process is presented, which shows that the measured spectral entities can be reconstructed by the Ritz method. It is also shown that the corresponding weighting factors can be physically interpreted as subdomains of constant temperature within the measurement area. Two different test cases are numerically analyzed, showing that the methodology allows for an approximation of the melt pool size while further assumptions remain necessary to reconstruct the actual temperature distribution.

Fine-Tuning Melt Pools and Microstructures: Taming Cracks in Powder Bed Fusion—Laser Beam of a non-weldable Ni-base Superalloy

Powder Bed Fusion – Laser Beam (PBF-LB) of high γ’ strengthened Ni-base superalloys, such as CM247LC, is of great interest for high temperature applications in gas turbines. However, PBF-LB of CM247LC is challenging due to the high cracking susceptibility during PBF-LB processing (solidification cracking) and heat treatment (strain age cracking, mostly caused by residual stresses). This study focuses on understanding the impact of process parameters on microstructure, residual stresses and solidification cracking. Laser power (P), speed (v) and hatch spacing (h) were varied while the layer thickness (t) was fixed. The melt pool size and shape were found to be key factors in minimizing solidification cracking. Narrower and shallower melt pools, achieved using a low line energy density (LED = P/v ≤ 0.1 J/mm), gave low crack densities (0.7 mm/mm2). A tight hatch spacing (h = 0.03 mm) resulted in reduced lack of fusion porosity. Electron backscatter diffraction investigations revealed that parameters giving finer microstructure with 〈100〉 crystallographic texture had low crack densities provided they were processed with a low LED. Atom probe tomography elucidated early stages of spinodal decomposition in the as-built condition, where Cr and Al cluster separately. The extent of spinodal decomposition was found to be affected by the LED and the hatch spacing. Samples with low LED and small hatch spacing showed higher degrees of spinodal decomposition. X-ray diffraction residual stress investigations revealed that the residual stress is proportional to the volumetric energy density (VED = P/(v. h. t)). Although low residual stresses can be achieved by using low VED, there is a high risk of lack of fusion. Hence, other parameters such as modified scan strategy, build plate pre-heating and pulsed laser mode, must be further explored to minimize the residual stresses to reduce the strain age cracking susceptibility.

Multi-scale microstructure manipulation of an additively manufactured CoCrNi medium entropy alloy for superior mechanical properties and tunable mechanical anisotropy

Laser beam powder bed fusion (PBF-LB) additive manufacturing (AM) technology has become a versatile tool for producing new microstructures in metal components, offering novel mechanical properties for different applications. In this work, enhanced ductility (∼55% elongation) and tunable mechanical anisotropy (ratio of ductility along vertical to horizontal orientation from ∼0.2 to ∼1) were achieved for a CoCrNi medium entropy alloy (MEA) by multi-scale synergistic microstructure manipulation (i.e., melt pool boundary, grain morphology and crystallographic texture) through adjusting key PBF-LB processing parameters (e.g., laser power and scan speed). By increasing the volumetric energy density (VED) from 68.3 to 144 J/mm3, the melt pool size enlarges, and the crystallographic texture transitions from <100>//BD to <110>//BD due to the maximum thermal flux direction changing for different melt pool dimensions, which affects the proportion of grains that have different growth directions. Moreover, excellent mechanical properties of 890 MPa ultimate tensile strength and ∼55% elongation to failure can be achieved for loading perpendicular to the build direction with only a ∼15% reduction in properties for loading along the build direction. The superior combination of mechanical properties is achieved by processing parameter-controlled strengthening of melt pool boundary interfaces, heterogeneous deformation induced strengthening through bimodal grain structures, and favorable grain orientation for dislocation slip and twinning formation, which was significantly more activated in the <110>//BD texture than in the <100>//BD. This study offers new insights into achieving multi-scale synergistic microstructure manipulation via PBF-LB for desired strength, ductility, and mechanical anisotropy in a CoCrNi MEA.

Multi-Scale Cu-Cr Composites Using Elemental Powder Blending in Laser Powder-Bed Fusion

Cu-Cr-based alloys are good candidates for thermo-electrical applications because of their excellent combination of mechanical, thermal, and electrical properties. However, tailoring their microstructure via traditional processes such as sintering and casting remains a challenging task. Herein, we introduce a strategy to fabricate multiscale Cu-25Cr composites. This is achieved through in-situ alloying from elemental powder blending in laser powder-bed fusion. The process is monitored layer-by-layer using synchrotron X-ray microtomography. We show that by changing the melt-pool size and its overlap, one can produce multiscale Cu-Cr composites with coarse (∼50µm) and fine Cr-spheroids (<500 nm) while controlling their spatial distribution. Coarse Cr particles are preferentially located at the melt pool boundaries while fine spherical Cr particles are distributed in the melt pool interior. Spatial variations in mechanical properties are revealed using nanoindentation mapping. This strategy opens new opportunities to optimize multiscale metal matrix composite through the manipulation of the scanning strategy.