Metal Additive Manufacturing Research Articles

Influence of wax addition on feedstock processing behavior in additive manufacturing of metals by material extrusion

The additive manufacturing of metals by material extrusion in a multi-step process (MEX-MSt/M) represents a special process variant of the commonly used material extrusion (MEX) and is based on the processing of highly filled polymer filaments. This technology uses the geometrical freedom and fast processing given by MEX to create individual metal parts after a debinding and sintering process in a cost and time-efficient way. The filaments for MEX-MSt/M are made by incorporating metal powders, such as aluminum, stainless steel, or bronze into a polymer matrix. Due to the challenges that are assigned to the processing of highly filled polymers, like the increased viscosity of the material or clogging of the nozzle, the binder materials have to meet several requirements. Therefore, waxes are often used to enable a better extrusion behavior for MEX; however, the addition of wax also affects other crucial processing properties of the filaments. In this work, the interactions of different types and amounts of waxes on thermal, mechanical, and rheological properties were investigated to create a better understanding of the alternating effects of wax addition into highly filled filaments for processing via MEX. The study demonstrated that an increase in wax contents resulted in both a significant decrease in ductility and an overall improvement in melt flowability. The choice of waxes also affected the particle–matrix interactions, partly leading to an improved wetting of the filler particles.

Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics

Abstract Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation, including computational fluid dynamics (CFD), is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure. This approach avoids solving the nonlinear Navier–Stokes equation numerically, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model.

Effect of Interlayer Machining Interventions on the Geometric and Mechanical Properties of Wire Arc Directed Energy Deposition Parts

Abstract Wire Arc Directed Energy Deposition (Wire Arc DED) has become a popular metal additive manufacturing technique for its capability to print large metal parts at a high deposition rate while being economically efficient. However, the Wire Arc DED process exhibits geometric inaccuracies resulting from the variability in the bead geometry and demonstrates heterogeneity in microstructure and mechanical properties. This study investigates the use of tailored periodic machining interventions during the Wire Arc DED process to address these shortcomings. The as-built geometry and surface finish, microstructure, and microhardness of multi-layer wall structures produced with and without machining interventions carried out at different temperatures are compared. The machining interventions are found to reduce the uncertainty in bead geometry evolution and significantly improve the surface roughness of the as-built walls, thus reducing the need for further post-processing of the wall surfaces. Although the microstructure constituents of the as-built wall structures with and without machining interventions are similar, the machining interventions result in finer grains in the interior of the part. Machining interventions are found to yield a statistically significant increase in microhardness, indicating increased strength compared to Wire Arc DED alone. In addition, the spread of the microhardness distribution is reduced in Hybrid-Wire Arc DED, indicating improved homogeneity of the grain size distribution compared to Wire Arc DED alone. The study shows that the proposed hybrid manufacturing technique has the potential to control and improve the geometric and mechanical properties of additively manufactured metal components.

Systematic investigation of performance and productivity in laser powder bed fusion of Ti6Al4V up to 300 µm layer thickness

Within the realm of metal additive manufacturing, laser powder bed fusion (PBF-LB) has maintained a dominant role by offering exceptional geometric freedom, fine feature resolution and fine microstructure features. However, low productivity still presents a bottleneck in the adaptation of PBF-LB in most industrial contexts. In recent literature, build-up rates have shown to improve notably when thicker powder layers are employed. However, systematic analyses linking processing parameters, productivity, microstructural state and mechanical properties are lacking. This study aims to fill this gap for Ti6Al4V alloy with powder layer thicknesses in the range of 60 µm to 300 µm, specifically using system-agnostic process parameters applicable to the majority of currently available commercial AM systems. The results revealed that Youngs modulus (∼110 GPa) and yield strength (∼1.1 GPa) remain comparable to ‘conventional’ PBF-LB Ti6Al4V throughout the investigated range of layer thickness. At the same time elongation to failure decreases from 11.4 ± 2.7% at 60 µm to 8.4 ± 1.1% at 180 µm and finally to 2.0 ± 0.3% at 300 µm, which was microscopically correlated with increased occurrence of lack-of-fusion porosity in layers exceeding 180um. It was also demonstrated that the changes in the parent β phase texture arising from process changes could have contributed to decreased ductility at thicker layers. Ultimately, while productivity increases with layer thickness up to 8.76 mm3/s at 300 µm, the achievable build rate appears to plateau around 300 µm layer height and require further expansion of the laser power characteristics to enable additional gains without compromising mechanical performance.

Metal additive manufacturing for particle accelerator applications

Metal additive manufacturing technologies are rapidly becoming an integral part of the advanced technological portfolio for the most demanding industrial applications. These processes are capable of fabricating three-dimensional components with near-net shape quality by depositing the constituent materials in a layer-by-layer fashion. This fabrication approach provides numerous advantages over conventional manufacturing methods, including enhanced design flexibility, reduced production costs and lead times, rapid prototyping, and the possibility to repair damaged parts. In recent years, the growing demand for novel accelerator components with improved performance characteristics, integrating structures such as drift tubes and internal cooling channels, has prompted the exploration of additive manufacturing in the field of particle accelerators. Radio-frequency components, beam intercepting devices, and vacuum systems have been prototyped using various metallic materials and additive manufacturing technologies, demonstrating performance levels comparable to the conventionally manufactured counterparts in preliminary tests. However, the absence of established qualification protocols and the uncertain reliability of additively manufactured parts under the demanding conditions typical of accelerator applications pose significant challenges to the integration of additive manufacturing processes into the fabrication practices of these components. This paper provides a comprehensive review of documented applications of metal additive manufacturing in particle accelerators, highlighting benefits, challenges, and opportunities for future improvements. The main requirements and currently available test setups for the assessment of additively manufactured components in applications involving ultrahigh vacuum and intense electromagnetic fields are also discussed. Published by the American Physical Society 2024

Optimizing energy modeling in PBF-LB/M metal additive manufacturing: a detailed analysis of resource and energy demand based on standard tensile test specimen

Additive manufacturing is an essential tool in modern production processes. Competitive quality and the increasing importance of local manufacturing have allowed companies to maintain their production despite supply chains disrupted by the pandemic. However, the rising awareness of society towards environmental and climate protection, the increasing demand for resource-efficient products, and recent developments in energy costs are leading to a rethink in manufacturing processes. Additive manufacturing offers great potential for resource saving components. This study uses standard tensile test specimens to analyze AM processes regarding energy and material flows. The results show a high dependence of energy demand on process time and are transformed into a data-based energy model. Compared to previous energy models, the accuracy can be significantly improved using model data in combination with specific and system-oriented approaches. In addition to system-related saving potentials, design-related optimization potentials can be identified. Innovative and highly resource-saving components can be designed with a design methodology adapted to the extended degrees of freedom of AM. Further saving and recycling potentials can be identified along the material flows. A long-term goal is optimizing and predicting of resource demand in additive manufacturing with a view to the entire product lifecycle.

Numerical analysis of machinability and surface alterations in cryogenic machining of additively manufactured Ti6Al4V alloy

Metal additive manufacturing (AM) technology has been utilized in many industries including automotive, aerospace, and medical. AM Ti6Al4V (Ti64) alloy is highly noticed for production of medical instruments such as dental implants and the machining process is mostly needed during the production or post-processing of these components. Numerical model, as a powerful tool, can be efficiently used for analyzing the machining process. A customized model was employed using a user-written subroutine in this work to evaluate machinability and microstructural changes in cryogenic machining of AM Ti64 alloy. For this purpose, the microstructural changes were simulated as the new numerical outputs. The numerical results of cutting forces, temperature, nano-hardness, and alpha lamellae thickness (grain size) were successfully verified by corresponding experiments from literature. Then, the impact of tool geometry (including rake and clearance angles, cutting edge radius, and nose radius) on the machinability performance was examined. It was found that, the variation of clearance and rake angles were more effective on depth of the hardened layer compared to the other parameters. Thickness of alpha lamellae phase near the machined surface and depth of the affected layer by nano-hardness changes were changed from 0.9 to 1.58 µm, and from 18 to 40 µm, respectively. Overall, it was concluded that the variation of insert positioning made by tool holder (change in rake and clearance angles) was an effective parameter on the process outputs when machining AM Ti64 alloy.

Effects of process parameters and geometry on dimensional accuracy and surface quality of thin strut heart valve frames manufactured by laser powder bed fusion

Laser powder bed fusion (LPBF) is one of the most popular metal additive manufacturing technologies, which has found its applications in high-value sectors such as aerospace and biomedical devices. Some recent studies on the LPBF of stents have demonstrated its feasibility in the fabrication in thin strut structures including heart valve frames, as used in transcatheter aortic valve implantation (TAVI) for the treatment of severe aortic stenosis. The state of the art method of laser cutting TAVI frame limits the scope for novel concepts which are made possible by additive manufacturing. However, the surface quality and dimensional accuracy of LPBF parts are lower than that produced by laser cutting. To start the development of new TAVI concepts, the feasibility of manufacturing thin frames by LPBF has been investigated based on the SAPIEN 3 frame by Edwards Lifesciences. In this study, simplified frames with strut size from 0.3 to 0.5 mm have been successfully manufactured. The effects of strut size, strut angle, laser power and scan speed on the dimensional accuracy and surface quality were systemically studied. In addition, a representative SAPIEN 3 frame was manufactured and assessed with high-resolution X-ray CT scans. Good overall dimensional accuracy and low porosity were obtained for the SAPIEN 3 frame. However, inclined struts were found to have relatively low dimensional accuracy and poor surface quality.

A surface quality optimization strategy based on a dedicated melt-pool control via the dashed-scan contouring technique in electron beam powder bed fusion

Electron beam powder bed fusion (EB-PBF) is a key metal additive manufacturing (AM) technology known for its high energy absorption and low heat stress. However, the high surface roughness of EB-PBF parts has limited its broader application. To address this issue, this paper proposes a dashed-scan contouring strategy to obtain lower surface roughness in EB-PBF. This strategy involves melting the contour in the form of segmented staggered scans and employing high-frequency jumping of the electron beam (EB) to melt multiple positions synchronously. A comprehensive analysis, combining experimental investigations and multi-physical simulations, elucidates the intrinsic connection between control processes and melt pool stability. Results show that by controlling the length-to-width ratio of the melt pool, regulating overlap distances between melt pools, and fine-tuning cooling times, balling phenomena and Plateau-Rayleigh instabilities can be suppressed. Additionally, these measures effectively mitigate irregularities in track formation when compared to the traditional process. Experimental validation demonstrates the efficacy of the approach, achieving reduced surface roughness (Ra) of thin-walled Ti-6Al-4 V parts from over 25 μm to below 12.6 μm, while enhancing dimensional accuracy and reducing melt anomalies at corners. The proposed dashed-scan contouring strategy opens new avenues for AM of highly reliable and intricate structures, such as lattice sandwich structures, thin walls, and internal flow passages.

The inverse heat placement problem in metal additive manufacturing: Why a rational approach is needed for powder bed fusion

Powder bed fusion (PBF) uses an energy beam to scan a powder bed surface, heat it locally, and consolidate the material to form a part. The choice of energy beam paths is typically based on user experience. Simple beam path strategies (e.g., raster or spiral) are used and the thermal field analysed post hoc. We refer to this approach as solving the direct problem, which involves many modelling and experimental iterations until a close to satisfactory, but not optimal, thermal field is achieved. In contrast, we propose a rational approach, solving an inverse problem to control heat placement, which we illustrate using laser beam PBF. Starting from the desired thermal field to be induced in the powder bed, we show how to formulate and solve the Inverse Heat Placement Problem (IHPP) so that a set of time-varying beam parameters (path, scan speed, energy delivered) is obtained in a single iteration. Solving the IHPP takes the craftsmanship out of beam path planning and puts it on a sound scientific and mathematical basis. This will enable a step change in the quality and complexity of customised macro-, meso-, and microscale properties of PBF built parts.

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

An approach to estimate the low cycle fatigue probabilistic curves of PBF-LB/M 316L steel from small size datasets using the remora optimization algorithm

The significant dispersion in fatigue properties is a prevalent attribute observed in metals manufactured by the Additive Manufacturing (AM). To ascertain the safety of components subjected to low cycle fatigue (LCF) loadings at a temperature of 550 °C, it is necessary to carry out a fatigue reliability assessment of AM metals. Nevertheless, the number of LCF life tests is limited owing to time-consuming nature of test and high total production cost of AM materials. Such a small size dataset cannot satisfy the criteria of the traditional reliability assessment method. This work aims to develop a novel method to evaluate the LCF reliability of 316L stainless steel manufactured by laser-based powder bed fusion of metals (PBF-LB/M) technology. Firstly, the Weibull model is coupled with the Coffin-Manson-Basquin relationship in order to characterize the dispersion of life data points at various strain levels. Secondly, reliability results from a small size dataset show a maximum relative error in fatigue life of less than 25 %, which is similar to results from a large size dataset and the traditional method using fatigue data from public sources. Among this step, the remora optimization algorithm is firstly applied to calculate the characteristic parameters of newly-proposed method. Thirdly, the LCF reliability of PBF-LB/M 316L based on the small size dataset is evaluated. The N95% is equivalent to 44 % of N50%. Finally, the PBF-LB/M 316L is compared with traditional 316L considering the reliability. The LCF properties of PBF-LB/M 316L is similar to the traditional 316L at 50 % reliability, however it’s slightly worse at 95 % reliability.

Effect of Pre-Heating on Residual Stresses and Deformation in Laser-Based Directed Energy Deposition Repair: A Comparative Analysis.

Laser-directed energy deposition (DED), a metal additive manufacturing method, is renowned for its role in repairing parts, particularly when replacement costs are prohibitive. Ensuring that repaired parts avoid residual stresses and deformation is crucial for maintaining functional integrity. This study conducts experimental and numerical analyses on trapezoidal shape repairs, validating both the thermal and mechanical models with experimental results. Additionally, the study presents a methodology for creating a toolpath applicable to both the DED process and Abaqus CAE software. The findings indicate that employing a pre-heating strategy can reduce residual stresses by over 70% compared to no pre-heating. However, pre-heating may not substantially reduce final distortion. Notably, final distortion can be significantly mitigated by pre-heating and subsequently cooling to higher temperatures, thereby reducing the cooling rate. These insights contribute to optimizing DED repair processes for enhanced part functionality and longevity.

The Production of Three-Dimensional Metal Objects Using Oscillatory-Strain-Assisted Fine Wire Shaping and Joining.

Material shaping and joining are the two fundamental processes that lie at the core of many forms of metal manufacturing techniques, including additive manufacturing. Current metal additive manufacturing processes such as laser/e-beam powder bed fusion and Directed Energy Deposition predominantly use heat and subsequent melt-fusion and solidification to achieve shaping and joining. The energy efficiency of these processes is severely limited due to energy conversion losses before energy is delivered at the point of melt-fusion for shaping and joining, and due to losses through heat transfer to the surrounding environment. This manuscript demonstrates that by using the physical phenomenon of lowered yield stress of metals and enhanced diffusion in the presence of low amplitude high frequency oscillatory strain, metal shaping and joining can be performed in an energy-efficient way. The two performed simultaneously enable a metal additive manufacturing process, namely Resonance-Assisted Deposition (RAD), that has several unique capabilities, like the ability to print net-shape components from hard-to-weld alloys like Al6061 and the ability to print components with a very high aspect ratio. In this study, we show this process's capabilities by printing solid components using aluminum-based metal alloys.

Design of a metal additive manufactured aircraft seat leg using topology optimization and part decomposition

PurposeThe lightweight design of aircraft seats can significantly improve fuel efficiency and reduce greenhouse gas emissions. Metal additive manufacturing (MAM) can produce lightweight topology-optimized designs with improved performance, but limited build volume restricts the printing of large components. The purpose of this paper is to design a lightweight aircraft seat leg structure using topology optimization (TO) and MAM with build volume restrictions, while satisfying structural airworthiness certification requirements.Design/methodology/approachTO was used to determine a lightweight conceptual design for the seat leg structure. The conceptual design was decomposed to meet the machine build volume, a detailed CAD assembly was designed and print orientation was selected for each component. Static and dynamic verification was performed, the design was updated to meet the structural requirements and a prototype was manufactured.FindingsThe final topology-optimized seat leg structure was decomposed into three parts, yielding a 57% reduction in the number of parts compared to a reference design. In addition, the design achieved an 8.5% mass reduction while satisfying structural requirements for airworthiness certification.Originality/valueTo the best of the authors’ knowledge, this study is the first paper to design an aircraft seat leg structure manufactured with MAM using a rigorous TO approach. The resultant design reduces mass and part count compared to a reference design and is verified with respect to real-world aircraft certification requirements.

A state-of-the-art review on metal additive manufacturing: milestones, trends, challenges and perspectives

A state-of-the-art review on metal additive manufacturing: milestones, trends, challenges and perspectives

Size matters: Exploring part size effects on microstructure, defects, and mechanical property in optimized laser powder bed fusion (L-PBF) additive manufacturing

In laser-based metal additive manufacturing (MAM), effective process optimization requires a thorough analysis of variations across the process window. Despite the common practice of utilizing small-sized coupons to optimize process parameters in laser-based MAM, this study challenges this convention. Through an analysis of microstructure and defect differences in Fe-Ni material parts produced via laser-powder bed fusion (L-PBF), the study reveals significant discrepancies in microstructure, defect and mechanical property based on part size. As part size increases, lack-of-fusion defects and cracks become more pronounced, resulting in a decrease in density from 8.11 g/cm3 to 8.02 g/cm3, leading to constrained grain growth with a decrease of grain size from 41.88 μm to 23.07 μm in the scanning plane. Each defect and microstructural difference was traced back to variations in thermal history based on part size. Finite element method simulations, highlighting differences in thermal history from scanning a single layer to the entire part, showed that an increase in scan length with a widened scan area enlarged the period of the thermal history cycle, leading to considerable cooling. This study underscores crucial considerations for future researchers engaged in process optimization for novel alloy systems using laser-based MAM processes.

Additively manufactured low-gradient interfacial heterostructured medium-entropy alloy multilayers with superior strength and ductility synergy

Metallic additive manufacturing (AM) offers near net-shape fabrication but often results in insufficient strength and/or ductility due to voids or cracks, coarse initial crystalline microstructure, insufficient volume fraction of precipitates and yet post-AM strengthening/toughening methods non-destructive to shape are generally lacking. Here, we report a laser-directed energy deposited (L-DED) medium entropy alloy (MEA) with a heterostructure (HS) comprising alternate layers of solid-solution and intermetallic-dispersed MEA that possess ultimate tensile strength of 1132.8 MPa and elongation of 50.6 %, corresponding to strength-ductility synergy higher than other medium- or high-entropy alloys reported. The multilayers were produced from a dual source of CoCrNi MEA powder and a powder mixture of the same MEA, Al and Ti with stoichiometry (CoCrNi)86Al7Ti7, and the remarkable strength-ductility synergy is achieved only after post-L-DED heat treatment, which causes Al and Ti to diffuse across the layers to form a low-gradient HS. The significant back stress due to the HS contributes to the high strength, while the high ductility results from a high strain-hardening rate suppressing necking. This study demonstrates the concept of using AM not just as a near net-shape fabrication method but also a unique tool to produce low-gradient HSs with superb mechanical properties, via the use of multiple powder sources combined with suitable shape-preserving post-fabrication heat treatment.

Enhanced Pool Boiling Heat Transfer with Porous Ti-6Al-4V-Coatings Produced by Cold Spray Metal Additive Manufacturing

In advancing industrial heat transfer mechanisms, surface coatings offer significant potential. This research elucidates the efficacy of the metal additive manufacturing Cold Spray deposition technique for producing enhanced boiling surfaces, specifically focusing on Ti-6Al-4V (Ti64) coatings on Aluminium substrates. This offers a rapid and low-cost fabrication method for producing lightweight enhanced boiling surfaces. The Cold Spray method is typically used to create dense metal deposits. Here, the process has been specially tuned to create highly inhomogeneous honeycomb-type porous Ti64 coatings. Critical Cold Spray deposition parameters, such as particle velocity, preheat temperature, and deposition rate have been identified to create repeatable porous coatings, with thicknesses of up to 3.0 mm achievable. Following deposition, several samples were subjected to systematic boiling heat transfer tests in a purpose-built pool boiling apparatus. Boiling curves were generated for the augmented Cold Spray surfaces as well as a bare surface, with the latter acting as a baseline to which enhancement levels were assessed. Initial data analysis shows that some of the tested surfaces exhibit a notable increase in boiling heat transfer coefficient and Critical Heat Flux (CHF). This enhancement is potentially attributed to increased surface area, increased nucleation site density, capillary wicking, and mitigation of lateral bubble coalescence, though excessive coating thickness may degrade heat transfer. In summary, the novel Ti64 surface structures developed using the Cold Spray deposition technique exhibits high potential for industries necessitating superior boiling heat transfer performance. Importantly, the manufacturing process is industrially scalable, offering the capacity to rapidly coat large areas at low cost compared with subtractive manufacturing other metal additive manufacturing methods.