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The continuous development of metal additive manufacturing (AM) promises the flexible and customized production, spurring AM research towards end-use part fabrication rather than prototyping, but inability to well control process defects and variability has precluded the widespread applications of AM. To solve these issues, process monitoring and control is a powerful approach. Recently, a variety of monitoring methods have been proposed and integrated with metal AM machines, which enables a large volume of data to be collected during the process. However, the data analytics faces great challenges due to the complexity of the process, bringing difficulties on developing effective models for defects detection as well as feedback control to improve quality. To overcome these challenges, machine learning methods have been frequently employed in the model development. By using machine learning methods, the models can be built based on the collected dataset, while it is not necessary to fully understand the process. This paper reviews the applications of machine learning methods in metal powder-bed fusion process monitoring and control, illuminates the challenges to be solved, and outlooks possible solutions. [ABSTRACT FROM AUTHOR]

Reinforcements in metal composites are usually dispersed at grain boundaries. We here show that the rapid solidification during laser additive manufacturing enables the spontaneous engulfment of reinforcing particles inside aluminum grains, which helps to decouple the stress concentration induced by grain boundaries and reinforcements. The additively manufactured TiB2-Al composite possesses 30% increase in tensile strength and nearly tripled ductility as compared with composite obtained by the traditional method. Our experimental investigations indicate that the intragranular dispersion of particles not only inhibits crack nucleation but also promotes strain hardening, leading to the remarkably enhanced mechanical properties. Metal composite with intragranular dispersion of reinforcing particles can be achieved through additive manufacturing, which alleviates stress concentration and possesses much-improved ductility. [ABSTRACT FROM AUTHOR]

To understand the physical mechanisms of the spattering and denudation phenomena in the laser powder bed fusion process, we develop a multiphase flow model by bi-directionally coupling the discrete element method and finite volume method, where the exchanges of both momentum and energy between the powder particles and gases are incorporated. It is the first time that the dynamic behaviours of both the gas phase and powder particles during the spattering and denudation phenomena are reproduced in computational modelling, which agree well with the published experimental observation. The metal vapour spouts out and decelerates sharply along the jetting direction while expanding radially, which induces remarkable vortex flows of the ambient gas. With comparative simulation cases, the vapour jetting and the consequent vortex flow are demonstrated to be dominant in the spattering and denudation phenomena, and the thermal buoyancy effect is proved to be negligible. Moreover, the influence of the jetting angle is investigated: no remarkable effect within the range of 60° - 120°; while larger than 150°, the accumulation zone is eliminated leading to a completely exposed denudation zone. Image, graphical abstract [ABSTRACT FROM AUTHOR]

The origin of dense dislocations in many additively manufactured metals remains a mystery. We here employed pure Cu as a prototype and fabricated the very challenging high-purity (>99.9%) bulk Cu by laser powder-bed-fusion (L-PBF) technique. We found that high-density dislocations were present in the as-built samples and these high-density dislocations were introduced on the fly during the L-PBF process. A newly developed multi-physics modeling was further employed to interpret the origin of these pre-existing dislocations, demonstrating that the compression-tension cycles rendered by the localized heating/cooling heterogeneity upon laser scanning are responsible for the residual high-density dislocations. The origin of dense dislocation structures in many as-built additively manufactured metals, which incurs a long-standing dispute, was found to be inherent to the additive manufacturing process. [ABSTRACT FROM AUTHOR]

One goal of modeling for metal Additive Manufacturing (AM) is to predict the resultant mechanical properties from given manufacturing process parameters and intrinsic material properties, thereby reducing uncertainty in the material built. This can dramatically reduce the time and cost for the development of new products using AM. We have realized the seamless linking of models for the manufacturing process, material structure formation, and mechanical response through an integrated multi-physics modeling framework. The sequentially coupled modeling framework relies on the concept that the results from each model used in the framework are contained in space-filling volume elements using a prescribed structure. This framework is implemented to show a prediction of the decrease in fatigue life caused by insufficient fusion resulting from low laser power relative to the hatch spacing. In this demonstration, powder spreading and thermal-fluid flow models are used to predict the thermal history and void formation in a multilayer, multi-track build with different processing conditions. The results of these predictions are passed to a cellular automaton-based prediction of grain structure. Finally, the predicted grain and void structure is passed to a reduced-order micromechanics-based model to predict mechanical properties and fatigue life arising from the different processing conditions used in the process model. The simulation results from this combination of models demonstrate qualitative agreement with experimental observations from literature, showing the appealing potential of an integrated framework. [ABSTRACT FROM AUTHOR]

Selective Electron Beam Melting (SEBM) is a promising powder-based metallic Additive Manufacturing (AM) technology. However, most powder-scale modeling efforts are limited to single track process, while it is also difficult to experimentally observe the interaction between tracks and layers. In this study, we develop an integrated modeling framework to investigate the SEBM process of multiple tracks and multiple layers. This approach consists of a Discrete Element model of powder spreading and a Computational Fluid Dynamics (CFD) model of powder melting. These two models exchange 3D geometrical data as a cycle to reproduce the manufacturing process of multiple tracks along various scan paths in multiple powder layers. This integrated modeling approach enables further understanding of how current tracks and layers interact with previous ones leading to inter-track/layer voids. It also incorporates more influential factors, particularly the layer-wise scan strategy. The inter-layer/track voids due to the lack of fusion are systematically discussed in light of our simulation results which qualitatively agree with experimental observations in literature. [ABSTRACT FROM AUTHOR]

Electron Beam Melting (EBM) is a promising powder-based metal Additive Manufacturing (AM) technology. This AM technique is opening new avenues for Functionally Graded Materials (FGMs). However, the manufacturing process, which is largely driven by the rapidly evolving temperature field, poses a significant challenge for accurate experimental measurement. In this study, we develop a novel multi-scale heat transfer modeling framework to investigate the EBM process of fabricating FGMs. Our heat source model mechanistically describes heating phenomena based on simulation of micro-scale electron-material interactions. It is capable of accounting for the material properties and electron beam properties that depend on experimental setup. The heat source model is utilized in the thermal evolution model of individual powder particles at the meso-scale to elucidate the melting and coalescing processes for mixed powder particles of different materials and different sizes. Another meso-scale simulation is conducted to evaluate the effective thermal conductivity of the original powder bed for the macro-scale model. A macro-scale heat transfer model is developed, in which the coalescence state is tracked to determine the effective material properties of the powder bed. Predictions of molten pool size are compared against published experimental results for validation. [ABSTRACT FROM AUTHOR]

The prediction of thermal stress and distortion is a prerequisite for high-quality additive manufacturing (AM). The widely applied thermo-mechanical model using the finite element method (FEM) leaves much to be improved due to their oversimplifications on material deposition, molten pool flow, etc. In this study, a high-fidelity modelling approach by linking the thermal-fluid (computational fluid dynamics, CFD) and mechanical models (named as CFD-FEM model) is developed to predict the thermal stress for AM taking into account the influences of thermal-fluid flow. Profiting from the precise temperature profiles and melt track geometry extracted from the thermal-fluid model as well as the remarkable flexibility of the quiet element method of FEM, this work aims at simulating the thermal stress distribution by involving physical changes in the AM process, e.g., melting and solidification of powder particles, molten pool evolution and inter-track inter-layer re-melting. Unlike the conventional thermo-mechanical analysis, in this approach, thermal stress calculation is purely based on a mechanical model where the thermal loads are applied by using a linear interpolation function to spatially and temporally map the temperature values from the thermal-fluid model's cell centres into the FEM element nodes. With the proposed approach, the thermal stress evolution in the AM process of single track, multiple tracks and multiple layers are simulated, where the rough surfaces and internal voids can be well incorporated. Moreover, a conventional thermo-mechanical simulation of two tracks with predefined track geometry is conducted for cross comparison. Finally, the simulated thermal stress distribution can rationally explain the crack distribution observed in the experiments. Unlabelled Image • Modelling thermal stress in AM using temperature profile from thermal-fluid model. • Rough surfaces and internal voids are incorporated. • Thermal stress concentrations due to voids and rough surfaces are revealed. • Simulation results of thermal stress can rationally explain the cracks in experiments. [ABSTRACT FROM AUTHOR]

In fusion‐based metal additive manufacturing (MAM), the high‐intensity energy input leads to serious evaporation, but how evaporation induces composition evolution and variation and further impacts microstructure and mechanical properties remain a knowledge gap. Here a model integrating composition evolution with molten pool dynamics is developed to reproduce temperature‐ and composition‐dependent evaporative losses and subsequent transport during laser melting. Together with comprehensive experimental characterizations and tests, the simulation results illustrate varying evaporation rates of different elements altering compositions, resulting in a 3D cirrus‐shaped concentration distribution, which significantly impacts the mechanical properties. The simulations reproduce the detailed composition evolution from surface evaporation to molten pool transport and reveal underlying mechanisms relating the composition, temperature, fluid flow, and cracking, which is challenging to observe experimentally. This study elucidates the critical role of evaporation‐induced composition evolution in determining microstructure and mechanical properties. In future alloy design for MAM, integrating initial composition and manufacturing parameters is imperative, where composition evolution simulation offers valuable guidance. [ABSTRACT FROM AUTHOR]

[Display omitted] The advent of additive manufacturing (AM) offers the possibility of creating high-performance metallic materials with unique microstructure. Ultrafine dislocation cell structure in AM metals is believed to play a critical role in strengthening and hardening. However, its behavior is typically considered to be associated with alloying elements. Here we report that dislocations in AM metallic materials are self-stabilized even without the alloying effect. The heating–cooling cycles that are inherent to laser power-bed-fusion processes can stabilize dislocation network in situ by forming Lomer locks and a complex dislocation network. This unique dislocation assembly blocks and accumulates dislocations for strengthening and steady strain hardening, thereby rendering better material strength but several folds improvements in uniform tensile elongation compared to those made by traditional methods. The principles of dislocation manipulation and self-assembly are applicable to metals/alloys obtained by conventional routes in turn, through a simple post-cyclic deformation processing that mimics the micromechanics of AM. This work demonstrates the capability of AM to locally tune dislocation structures and achieve high-performance metallic materials. [ABSTRACT FROM AUTHOR]

In the metal additive manufacturing process, the exposure to oxygen and its incorporation into the melt pool are usually deemed unfavorable, but cannot be completely eliminated. Yet, the understanding of this inevitable process remains limited. This work aims to shed light on the effect of oxygen content on melt pool dynamics through multiphysics thermal-fluid flow simulations of the laser powder bed fusion process. Our simulations reveal that oxygen sources from the powder, base plate and oxygen absorption from the atmosphere influences the melt pool dynamics. Although changes in oxygen content barely affect melt pool dimensions, they induce huge differences in the melt pool dynamics and the corresponding material composition distribution within the melt pool. Moreover, our model further clarifies and explains observed experimental phenomena. We demonstrate that the melt pool flow characteristics are responsible for the formation of oxygen-rich streaks observed in experiments regardless of inward or outward Marangoni circulation, while previous experimental studies attributed that to the outward circulation. Additionally, we show that sulfur content minimizes the effect of oxygen on Marangoni flow in iron alloys, and thus leads to the apparent consistency of surface roughness for additively manufactured iron alloys. This work is a fundamental development towards modeling for additive manufacturing under reactive atmospheres and provides unprecedented details on the effects of oxygen on melt pool dynamics. Consequently, this work can further offer practical guidance on powder reuse and adjusting manufacturing parameters for reused powders, thereby improving the sustainability of additive manufacturing. [Display omitted] [ABSTRACT FROM AUTHOR]

Gases in the atmosphere inevitably react with the melt pool during metal additive manufacturing (AM). Oxygen is particularly reactive, and excessive uncontrolled oxidation is detrimental, so most machines purge the chamber with inert gases, which can minimize but not eliminate such reactions. Alternatively, some users exploit the gas–liquid metal reactivity as an opportunity to introduce beneficial precipitates into the melt pool ("reactive AM"). However, the gas–liquid metal reaction and mechanisms in both scenarios remain unclear. Experimental works hitherto provide different explanations to the same phenomena. Therefore, this work seeks to clarify the mass transfer process of oxygen during metal AM through high-fidelity modeling by considering the competition between diffusion and chemical reaction, suboxide evaporation, and the influence of the vapor plume. The simulation results, validated with experiments, provide consolidated insights into the oxygen evolution behavior during metal AM. Counterintuitively, higher melt pool temperatures do not necessarily lead to greater oxidation rates during processing. The melt pool has regions of high and low oxygen gains due to temperature-dependent reaction regimes, with concurrent oxygen loss from evaporation of metal suboxides. Thus, the net oxygen flux varies for different materials, and the oxygen content cumulatively changes as multiple tracks are scanned. Overall, this work provides useful guidance to the AM community that seeks to ameliorate or exploit the inevitable gas–liquid interaction in metal AM. Cost-saving measures may be possible for determining the purity of shielding gas used in AM, and physics-guided measures can be taken to limit or control gas–liquid metal reactions. [Display omitted] [ABSTRACT FROM AUTHOR]

The process-structure–property relationship for additive manufacturing (AM) is typically derived starting from the temperature profile which can be achieved by the meso-scale thermal-fluid flow simulation with huge computational cost. We propose a data-driven prognostic approach with specialized physical constraints to rapidly predict the temperature profiles. The dataset is constructed from the physics-based thermal-fluid flow simulation results under different manufacturing parameters. The temperature field around the molten pool region is statistically characterized by the function parameters of the individual isotherms, which are essentially the output of the data-driven model based on the input manufacturing parameters, while the temperature field is reconstructed using the interpolation approach based on the predicted isotherms. The data-driven predicted temperature profiles are validated against those from the thermal-fluid flow simulations, and then further applied in the thermal stress and grain growth simulations, of which the results are compared with those using the temperature profile directly from thermal-fluid flow simulations. The results demonstrate that our data-driven approach is highly feasible in predicting the geometry features of the isotherms and temperature profiles around the molten pool regions. [Display omitted] • A physically-informed data-driven model to predict temperature field in AM. • A small amount of training data from high-fidelity thermal-fluid flow simulations. • Temperature field is represented by isotherms instead of a patternless field. • The data-driven model is seamlessly linked with other physics-based models. • Good agreement with ground truth in prediction of thermal stress and grain growth. [ABSTRACT FROM AUTHOR]

Metallic parts fabricated by additive manufacturing (AM) usually exhibit unique microstructures and non-negligible residual stresses compared with the counterparts produced by conventional manufacturing. These inherent microstructural factors strongly affect the mechanical response of the as-built AM parts. In this study, we focus on the strain localization behavior of 316L stainless steel produced by laser powder-bed-fusion. In-situ tensile tests under a scanning electron microscope are performed, and the digital image correlation method is used to measure the strain distribution combined with electron backscatter diffraction. Meanwhile, a dislocation-based crystal plasticity finite element model incorporating residual stresses is developed to study the origins of the strain localization in the AM material. The results indicate that strain localization in AM materials is closely associated with microstructural features, encompassing behaviors related to slip activities, interactions with neighboring grains and dislocation evolutions. Additionally, the columnar grain features also render the strain distribution sensitive to the loading direction. The strain localization is serious in some small grains with high residual stresses, while in large grains the effect is less significant. These factors collectively contribute to the increasing likelihood of strain localization occurring in the AM microstructures with heterogeneous grain size and texture distribution. This work provides detailed insights into the strain localization in AM materials and would facilitate the manufacturing parameter optimization of AM materials by tuning the microstructure to reduce deformation inhomogeneity. • Strain localization in AM material is investigated by experiments and modeling. • A crystal plasticity model is developed to incorporate grain-scale residual stresses. • The correlation between AM microstructure and strain localization is revealed. • Slip activity, grain interaction and dislocation evolution are studied statistically. • High residual stresses are found to induce strain localization in small grains. [Display omitted] [ABSTRACT FROM AUTHOR]

Direct energy deposition (DED) is a promising additive manufacturing technology for large-scale fabrications of high-value components. Grain structure control is challenging but meaningful for achieving desirable mechanical properties. A multi-scale three-dimensional (3D) Finite Volume Method-Cellular Automaton (CA-FVM) model is developed. The grain structure evolution in the transition-mode melting is investigated, and the simulated grain structures show fairly good qualitative and quantitative agreement with the experimental results. The influences of laser power and scanning speed on the formed grain structure are examined. A progressive columnar-to-equiaxed transition (CET) is found. The elongated grain is the primary grain morphology, even with the CET. The effects of high temperature gradient on the development of columnar structure are difficult to overcome. Moreover, nanoparticle reinforcement is numerically investigated as a promising technique to realize the site-specific grain structure control by interfering with the columnar growth. We expect this study to provide a deeper understanding of the DED-produced grain structure and improve confidence in the site-specific structure control. Unlabelled Image • A multi-scale three-dimensional Finite Volume Method-Cellular Automaton model is developed. • Columnar-to-equiaxed transition in transition-mode melting is simulated. • Elongated grain is primary grain morphology even with equiaxed nucleation. • Nanoparticle refinement is an efficient way to promote massive nucleation of equiaxed grains. [ABSTRACT FROM AUTHOR]

The packing density of the powder layer plays a key role in the final quality of the parts fabricated via powder-bed-based (PBB) additive manufacturing. This paper presents a combined experimental and computational modeling study on the scraping type of powder-spreading process, in order to understand the fundamental mechanisms of the packing of the powder layer. The deposition mechanisms at the particulate scale, including particle contact stress and particle velocity, are investigated, using the discrete element method, while the macro-scale packing density is validated by experiments. It is found that there is a stress-dip at the bottom of powder pile scraped by the rake. This stress-dip makes the powder particles uniformly deposited. Three kinds of deposition mechanisms dominating the powder-spreading process are identified: cohesion effect, wall effect, and percolation effect. The cohesion effect, which leads to particle agglomerations and thus reduces the packing density, becomes stronger with the decrease of particle size. The wall effect, which leads to more vacancies in the powder layer, becomes stronger with the decrease of layer thickness or the increase of particle size. The percolation effect exists in bimodal powder particles, which leads to particle segregation within the powder layer and thus reduces the packing density. The three kinds of deposition mechanisms compete with each other during the powder-spreading process and make combined effects on the packing density of the powder layer. Image 1 [ABSTRACT FROM AUTHOR]

Metal additive manufacturing has seen extensive research and rapidly growing applications for its high precision, efficiency, flexibility, etc. However, the appealing advantages are still far from being fully exploited, and the bottleneck problems essentially originate from the incomplete understanding of the complex physical mechanisms spanning from the manufacturing processes, microstructure evolutions, to mechanical properties. Specifically, for powder-fusion-based additive manufacturing such as laser powder bed fusion, the manufacturing process involves powder dynamics, heat transfer, phase transitions (melting, solidification, evaporation, and condensation), fluid flow (gas, vapor, and molten metal liquid), and their interactions. These interactions induce not only various defects but also complex thermal-mechanical-compositional conditions. These transient conditions lead to highly non-equilibrium microstructure evolutions, and the resultant microstructures, together with those defects, can significantly alter the mechanical properties of the as-built parts, including strength, ductility and residual stress. We believe that the most efficient approach to advance the fundamental understanding is integrating in-situ experimentation and high-fidelity modeling. In this review, we summarize the state of the art of these two powerful tools: in-situ synchrotron experimentation and high-fidelity modeling, and provide an outlook for potential research directions. [Display omitted] • In-situ experimentation and high-fidelity modeling synergize in understanding AM. • Critical physical phenomena in metal additive manufacturing (AM) are described. • The basic principles, capability and applications of these two tools are reviewed. • Future research directions to further understand metal AM are discussed. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a highly promising technique for producing near-net-shape high-value aerospace components with intricate geometries. However, due to the susceptibility to hot cracking, high-volume-fraction Ni 3 (Al,Ti)-type precipitation-strengthened Ni-base superalloys typically used for these applications are usually difficult or even infeasible to manufacture via AM. Previous metallurgical methods to mitigate hot-cracking in AM usually compromise strength, resulting in a trade-off between hot-cracking resistance and strength. Here, we overcome this trade-off in AM of a multicomponent Ni-rich precipitation-strengthened high-entropy alloy (MNiHEA) Ni 46.23 Co 23 Cr 10 Fe 5 Al 8.5 Ti 4 W 2 Mo 1 C 0.15 B 0.1 Zr 0.02 (at%): synergizing remarkable hot-cracking resistance and ultrahigh strength. Crack-free MNiHEA is successfully fabricated via laser powder bed fusion (LPBF) without preheating, despite its high (Al+Ti) content of ∼7.4 wt%. This remarkable resistance to hot cracking arises from the comparatively low critical solidification range, the small average solidification cracking index, the suppression of the intermetallic phases in solidification, and the moderate hardening rate induced by the nanoprecipitates in aging, which are intrinsically imparted by thermodynamic and mechanical characteristics. The yield strength of the as-built-to-aged MNiHEA reaches ∼1.2 gigapascal, together with acceptable ductility. This ultrahigh strength outperforms its cast-to-aged counterpart by more than one-third and existing commercial superalloys CM247 and IN738LC. Such pronounced enhancement in strength is attained through multiple strengthening mechanisms, including solid-solution hardening, dislocation hardening, precipitation-hardening and grain-boundary strengthening. Our results demonstrate that intrinsic hot-cracking resistance can be utilized as a new metallurgical concept for mitigating hot-cracking without compromising strength in AM of high-temperature materials such as HEAs and superalloys. [Display omitted] [ABSTRACT FROM AUTHOR]

• A multi-physics gas-particle multi-phase flow model coupled with a laser ray-tracing heat source model for DED. • Prediction of the particle temperature, velocity, and concentration distribution, and laser energy loss. • Laser attenuation with the consideration of laser reflection. • Powder catchment efficiency reaches highest (27-30%) around focal plane. • Laser attenuation linearly changes with power feed rate or carrier gas flow rate. [Display omitted] As a promising additive manufacturing technique, laser-based directed energy deposition has been widely applied in part fabrication, surface cladding, and part restoration. During the powder feeding process, the gas, particles, and laser interact and further affect the molten pool flow. To study the powder feeding process, a multi-physics gas-particle multi-phase flow model coupled with a laser ray-tracing heat source model is developed to predict the powder particle temperature, velocity, and concentration distribution as well as laser energy loss, which is then validated against experimental results. The single-nozzle simulation shows that powder particles out of the nozzle follow a radial Weibull distribution instead of a 2D Gaussian distribution from the inlet of the nozzle. In the four-nozzle powder feeding process, the particles converge further after passing the focal plane, and setting the focal plane above the molten pool surface could potentially increase the powder catchment. Moreover, the laser ray-tracing model incorporates laser reflection and laser incident angle into laser energy loss calculation, which would be more accurate than those models based on the Beer-Lambert law. Additionally, by increasing the powder feed rate or decreasing the carrier gas flow rate, the laser energy loss linearly increases. The predicted powder velocity, temperature distribution, and attenuated laser can be further implemented into the molten pool flow model to more comprehensively study the interaction between particles and the molten pool in the future. [ABSTRACT FROM AUTHOR]

[Display omitted] Understanding the complex multi-phase interactions are vital for defects reduction in additive manufacturing (AM) of metal matrix composites. In this study, we propose a high-fidelity model to reveal the dynamics of molten pool and reinforcing solid particles during the AM process, using the resolved Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) with bi-directional momentum and energy exchange. Our model is validated against the electron beam melting (EBM) experiments of tungsten-copper composites fabricated with elemental powder blends. The results demonstrate that the interface effect, including the dynamic wetting phenomena and Laplace pressure, play significant roles in the dynamics of reinforcing solid particles. On the other hand, the existence of reinforcing solid particles in the molten pool changes the molten pool size and alters the flow field during the melting process. Although the interface effect causes tungsten particle agglomeration at single track surface, the layer-wise deposition scheme with proper layer thickness eliminates the cluster and promotes the uniform tungsten distribution in the densified bulk sample, which shows the capability of AM to achieve spontaneous dispersion of reinforcing solid particles in the metal matrix. This work provides unprecedented details about the multi-phase dynamics in metal matrix composite AM process. [ABSTRACT FROM AUTHOR]

• Novel compact plate-fin heat exchangers fabricated by laser powder bed fusion (LPBF) • Experimental and numerical investigation of different plate-fin heat exchangers • Plate-fin heat exchanger with twisted fins exhibits superior thermal performance • Improved rectangular fins improve coefficient of performance and overall performance Plate-fin heat exchanger (PFHX) is one of the vital compact heat exchangers due to its high heat transfer area per unit volume and promising potentials in industrial applications where dimensions and weight are limited. Additive manufacturing presents great potentials for fabricating novel compact heat exchangers. In this work, three types of compact heat exchangers including a PFHX with twisted fins, a PFHX with improved rectangular fins and a PFHX with conventional rectangular fins are manufactured using the laser powder bed fusion (LPBF) technology. The experimental tests together with numerical simulations are conducted to evaluate the thermal performance and pressure drop of the heat exchangers. The results show that PFHX with twisted fins exhibits superior thermal performance among the three heat exchangers, and the PFHX with improved rectangular fins has the highest experimental coefficient of performance (COP) and provides outstanding overall performance. Furthermore, the impact of LPBF accuracy on the performance of heat exchanger are discussed to provide guidance for future designs. [ABSTRACT FROM AUTHOR]

• A new synthetic microstructure generation approach is proposed for AMed metals according to the characteristics of grain growth in the fabrication process. The constitutive relation of individual grains is provided by the single-crystal-scale plasticity model. • To reduce the computational cost, a polycrystal-scale plasticity model is also established. The homogeneous elastic moduli tensor is computed based on Mori-Tanaka's theory, while the plastic deformation is described by the equivalent grain set. This paper presents a multi-scale modelling framework to evaluate the structure-property relationship of metallic materials fabricated by powder-bed additive manufacturing (AM) technique based on crystal plasticity finite element methods. In this framework, a new synthetic microstructure generation approach is proposed to reconstruct micro-scale models of AMed metals according to the characteristics of grain growth in the fabrication process. The constitutive relation of individual grains in the micro-scale reconstructed models is described with the single-crystal-scale plasticity model. Meanwhile, to reduce the computational cost, a polycrystal-scale plasticity model is also established. The homogeneous elastic moduli tensor is computed according to Mori-Tanaka's theory, while the plastic deformation is described by the equivalent grain set. The proposed multi-scale modelling framework is validated against experiments, where the as-built Ti-6Al-4V samples fabricated by selective laser melting (SLM) are tested under uniaxial tensile, compressive, and cyclic loadings. The presented experimental and computational study demonstrates the capability of the proposed multi-scale modelling framework in the structure-property analysis of AMed metals. Image, graphical abstract [ABSTRACT FROM AUTHOR]

An integrated modeling framework coupling the discrete element method for powder spreading, finite volume method for powder bed melting, and an extended cellular automaton method for grain structure evolution during solidification is proposed. In this framework, the initial grain structure of both the substrate and metal powders can be taken into account and used to capture epitaxial and competitive grain growth. The framework is used to provide an in-depth understanding of microstructure development in Ti-6Al-4V during the selective electron beam melting process. The complex process of grain growth during deposition of multiple tracks and multiple layers is modeled through an analysis restarting scheme. The epitaxial growth of grains from pre-existing grains, in particular the grains of partially melted powders, is reproduced. The mechanism of microstructure development within the overlap region of consecutive tracks and layers for various scan strategies is revealed. The simulation results are in qualitative agreement with experimental observation in the literature. The proposed modeling framework is a powerful tool to guide optimal process parameters that lead to designed, site-specific microstructure control and therefore to tailored mechanical properties of parts fabricated by the powder bed fusion additive manufacturing process. Unlabelled Image • A powder-scale integrated modeling framework is developed for microstructure prediction. • The effect of initial microstructure on solidification microstructure is identified • Varying the scan strategy effects the texture. • A "skin-layer" is shown to epitaxially grow from partially melted powder. [ABSTRACT FROM AUTHOR]

Powder spreading is an essential procedure in powder-bed-based additive manufacturing, and the resultant packing quality of the powder layer has important effects on the quality of the final products. In this work, the counter-rolling-type powder spreading is investigated by experiments and numerical simulations. Non-invasive in-situ measurements are performed to evaluate the packing qualities of the powder layer such as surface roughness and packing density, where the effect of the spreading speed is studied. It is found that both the surface quality and packing density of the powder layer decrease with the increase of spreading speed. Besides, the sensitivity of the surface roughness of the powder layer increases with the spreading speed, i.e., the higher the spreading speed is, the more remarkably the surface quality decreases. Numerical simulations using the discrete element method are performed to investigate the dynamics of the powder spreading in terms of the velocity, contact force and coordination number of powder particles, providing new insight to the physical mechanisms underlying the counter-rolling-type powder spreading at particulate scale. Image 1 • Counter-rolling-type powder spreading process in additive manufacture is studied. • Surface roughness and packing density of thin powder layer are tested in-situ. • Packing qualities of powder layer decrease with increase of spreading speed. • The higher the spreading speed, the faster the decrease rate of surface quality. • Increase of pressure in powder pile is not beneficial for the packing qualities. [ABSTRACT FROM AUTHOR]

• A powder-scale multi-physics model using the Finite Volume Method is developed to study the direct energy deposition process. • The model can simulate cladding track geometry rapidly and accurately. • The influences of the process parameters on the track geometry are analysed in detail using an analysis of variance method. • A Gaussian process regression model is developed to predict the cladding track geometry based on the simulation results. Direct energy deposition (DED) is an effective method to fabricate complex metal thin-wall structures. The geometrical dimensions of the cladding track have significant influence on the dimensional precision of final components. In this study, a powder-scale multi-physics model using the Finite Volume Method (FVM) is developed to study the direct energy deposition process. The mass transfer, phase transformations and heat transfer in the DED process are incorporated and the geometrical characteristics of a single cladding track can be rapidly predicted. The influences of the process parameters including laser power, powder feed rate and scanning speed on the track width and height are analyzed in detail using an analysis of variance (ANOVA) method. Based on the simulation results, a Gaussian process regression (GPR) model is developed to predict the geometrical characteristics of cladding tracks under different manufacturing parameters. Finally, both the multi-physics model and the GPR model are validated by single track deposition experiments. The results show that the proposed multi-physics simulation results are in good agreement with the experimental results and can reveal the qualitative relationship between parameters and track geometry. The GPR model is able to predict the geometrical characteristics of single cladding tracks. [ABSTRACT FROM AUTHOR]