Directed Energy Deposition Additive Manufacturing Research Articles

Microstructure, microhardness, tensile and fatigue investigation on laser shock peened Ti6Al4V manufactured by high layer thickness directed energy deposition additive manufacturing

Thin layer thickness in additive manufacturing (AM) has always been a hindering factor leading to long completion times. Increasing layer thickness is accompanied by a higher tendency of surface cracks and inferior mechanical properties. These can be improved with laser shock peening (LSP). Emphasizing on the high layer thickness, this paper elucidates the high-layer thickness laser directed energy deposition (LDED) AM and the effects of LSP on the fabricated parts. Primary focus was given to the investigation of surface microcracks, microstructures, tensile and fatigue properties. Ti6Al4V bulk sample was prepared using the LDED process. Tensile, fatigue, and other samples were extracted to investigate the above-mentioned properties using different characterization tools such as optical microscope, scanning electron microscope, Vickers microhardness tester, and universal testing machine. Results showed proper depositions without major defects despite the high-layer thickness. Micro-cracks were limited to the surface only, and typical Widmanstätten patterns with parallel α lath and mixed α+β microstructures are observed. Yield strength (YS) varied from 730 MPa to 940 MPa, while ultimate tensile strength (UTS) ranged from 799.5 MPa to 953.75 MPa. Even though laser peening significantly improved micro-hardness by 49.81 % on average, not all fatigue samples observed enhanced fatigue life. A maximum improvement of 40.34 % in fatigue performance was observed in the LDED+LSPed samples. The strain hardening, plastic deformation, and grain refinement during the LSP process contributed to the enhanced mechanical properties. Some samples’ reduced fatigue life may be due to the anisotropic nature of additively manufactured parts, internal defects, and surface cracks on the specific samples. Considering high layer thickness, vertical orientation of samples, and as-prepared samples, our research shows a promising high layer thickness additive manufacturing process. The current research can be used as a base for further study on other properties and characteristics.

Microstructural evolution and tensile properties of direct energy deposited Ti-4Al-5Co-0.25Si alloy during heat treatment

Additive manufacturing (AM) stands as an advanced manufacturing process, successfully utilized for the fabrication of high-value titanium components. However, these components have yet to find widespread use in industrial applications due to their inadequate performance when compared to conventionally processed parts. Consequently, there is a pressing demand to develop new alloys and microstructural control strategies to overcome this limitation. Here, we have designed a cost-effective Ti-4Al-5Co-0.25Si alloy for use in laser-based direct energy deposition additive manufacturing. The selection of solute elements has resulted in both solute strengthening and grain refinement. Additionally, the implementation of a heat-treatment schedule has allowed for control over the phase constituents and their stability. The as-fabricated alloy exhibits a prior-β grain size of ∼50 μm, α plate thickness of ∼0.6 μm, and tensile strength of 1145 MPa. Subsequent heat treatment induces the formation of subgrains, discontinuous grain boundary α and metastable β phases, and ultrafine martensitic features ranging from 40 to 100 nm. Moreover, this heat treatment improved the tensile strength of the alloy to 1500 MPa while maintaining an elongation of ∼5%. Importantly, the proposed hierarchical structured alloy offers an economical alternative, outperforming commercially available titanium alloys. This study presents a new direction for further research into alloy and microstructural design, aiming at the development of low-cost, high-performance titanium alloys suitable for additive manufacturing.

Solidification behavior and texture of 316L austenitic stainless steel by laser wire directed energy deposition

The solidification behavior and crystallographic texture of 316L austenitic stainless steel builds fabricated via laser-wire directed energy deposition additive manufacturing (AM) were investigated. Shielding gas set-up and build type (single-track vs. multi-track) were varied, which led to changes in the solidification conditions and the final build compositions. Primary δ-ferrite solidification is predicted based on equilibrium thermodynamic predictions of the bulk feedstock composition. However, the increased solidification velocity of the AM process promotes a solidification mode transition from primary δ-ferrite to primary austenite. Skeletal δ-ferrite and lathy δ-ferrite associated with primary δ-ferrite solidification and interdendritic δ-ferrite associated with primary austenite solidification were observed among the laser-wire directed energy deposition walls. Skeletal and interdendritic δ-ferrite exhibited a (001)δ // (001)γ parallel relationship with austenite and a {001} solidification texture aligned with the build direction for both austenite and δ-ferrite. Lathy δ-ferrite exhibited a Kurdjumov-Sachs orientation relationship, (101)δ//(11¯1)γ, with austenite due to austenite nucleation in the solid-state. It is shown that the solidification behavior of AM 316L is accurately represented by as-built composition data and a dendrite growth model, which combined, encompasses the compositional and solidification condition impacts on the solidification pathway.

Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

Abstract High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain of a part. However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the regions near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This paper presents a data-driven model that uses the Fourier Neural Operator to capture the local temperature evolution during the AM process. Beside MSE, the model is also evaluated using the R2 metric, which places great weight on the regions where the temperature changes significantly than MSE. The model was trained and tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition AM process. The results shows that the model maintains 0.983-0.999 R2 over geometries not included in the training data, which is higher than Convolutional Neural Networks and Graph Convolutional Neural Networks we implemented, the two widely used architectures in data-driven predictive modeling.

Microstructure and elastocaloric effect of NiTi shape memory alloy in-situ synthesized by laser directed energy deposition additive manufacturing

The NiTi SMAs were successfully in-situ synthesized by LDED additive manufacturing, and the microstructural evolution and elastocaloric effect of the NiTi SMAs along parallel and perpendicular to the build direction (BD) were investigated. The NiTi SMAs at room temperature consisted of B2 austenite, B19’ martensite and the precipitation phase Ti2Ni, which was diffusely distributed within the B2 grains and at grain boundaries. Grain morphology that crosses the molten pool boundary and elongates along the thermal gradient is found in the plane parallel to the BD direction. NiTi SMAs loaded perpendicular to the BD direction have less energy loss and fewer stress mechanical training times to obtain fully recoverable stress-strain behavior, which shows better stress-strain response behavior than that loaded parallel to the BD direction. The maximum strain and adiabatic temperature changes follow a monotonically increasing trend from the maximum stress when the compressive stress was applied along parallel and perpendicular to the BD direction and rapidly removed. When the 900 MPa stress was removed, the adiabatic temperature changes reached −5.2 K and −5.9 K, respectively. The degradation of the elastocaloric effect of loading parallel to the BD direction reaches 15% after 100 stress-strain cycles (800 MPa). In contrast, the degradation of loading perpendicular to the BD direction is negligible. This work provides a new idea to realize the preparation of NiTi SMAs and to study the elastocaloric effect of NiTi SMAs.

Towards a self-healing aluminum metal matrix composite: Design, fabrication, and demonstration

This paper presents a novel approach to designing and synthesizing a self-healing aluminum-based metal matrix composite (MMC) at the macro-scale. The composite comprises an Al 5083 matrix embedded with low melting point particles (LMPPs) that act as healing agents. A two-step electroless micro-encapsulation process is developed to create LMMPs with a diffusion and thermal barrier designed to protect the Zn-8Al core with a Co-P shell. The MMC is fabricated using spark plasma sintering. Following controlled total fracture under tension, external compressive force is applied during heat treatment to heal the fracture effectively. The evolution of phases and interfaces is characterized using electron microscopy, and transient liquid phase bonding (TLPB) is identified as the fracture-healing mechanism, facilitated in areas with sufficiently high Zn concentration to fill the crack. The design can be expanded to incorporate other matrix and LMMP materials, mechanical crack volume reduction by integrating shape memory alloy (SMA) reinforcement during MMC synthesis, and processing of the self-healing MMC using Directed Energy Deposition additive manufacturing.

Improving the fatigue property of 316L stainless steel through direct energy deposition technology

In this study, to improve the fatigue strength of coarse grain 316L stainless steel, the hard 17-4ph martensitic stainless steel was deposited on the surface of hot rolled 316L based on direct energy deposition (DED) additive manufacturing technology. The 17-4ph deposition and 316L substrate are well connected without any cracking phenomenon which lays the foundation of good mechanical properties. The 17-4ph deposition presents a gradient structure, its fraction of body centered cubic (BCC) phase gradually increases from 0 vol% of substrate to 99.3 vol% of top surface, and its grain size gradually decreases from 53.2 μm near the substrate to 5.1 μm of the top surface, leading to the hardness of deposition gradually increases from 187 Hv to 436 Hv. The fatigue property of 17-4ph coated 316L is significantly improved. Specifically, the fatigue strength in high cycle fatigue (HCF) regime (at the fatigue life of 2 × 106 cycles) is 64 % higher than coarse-grained 316L, while the increment in low cycle fatigue (LCF) regime (at the fatigue life of 104 cycles) is up to 100 %. The improvement in LCF regime of 17-4ph coated 316L is higher than that of HCF regime, which is exactly opposite to the improvement in HCF and LCF regime of samples fabricated by traditional surface strengthening processes. Based on advanced characterization techniques, the potential strengthening mechanism in 17-4ph coated 316L was studied. It is expected that this study can provide reference for the application of additive manufacturing technology in the field of surface strengthening.

Coaxial color channel focus evaluation to estimate standoff height in directed energy deposition additive manufacturing

Coaxial color channel focus evaluation to estimate standoff height in directed energy deposition additive manufacturing

Unraveling microstructure and mechanical response of an additively manufactured refractory TiVHfNbMo high-entropy alloy

Refractory high-entropy alloys (RHEAs) have attracted considerable interest due to their elevated melting points and exceptional softening resistance. Nevertheless, the ambient-temperature brittleness and inadequate high-temperature oxidation resistance commonly restrict the processability of RHEAs. Direct energy deposition (DED) additive manufacturing technology is ideal for fabricating refractory alloys due to design flexibility and oxygen-free environment. In this work, a novel Ti41V27Hf13Nb13Mo6 RHEA was successfully manufactured by DED, and a comprehensive investigation was conducted to explore the microstructure evolution and mechanical response during tension. The as-deposited RHEAs exhibit a grain-size graded microstructure with a body-centred-cubic (BCC) matrix and precipitates. Increasing laser energy density suppressed the grain boundary precipitation, effectively enhancing the ambient-temperature tensile ductility to ∼11.3%. The optimized specimens achieved an unprecedented yield strength of ∼1.2 GPa among the DEDed RHEAs, which can be attributed to a significant solid solution strengthening from the volume misfit of 5.03%. We revealed that dislocation interactions maintained the working hardening capacity. Moreover, in-situ characterization indicated that slip transfer, grain rotation, and kinking accommodated the plastic deformation. Crack nucleation was caused by slip inhibition at grain boundaries and dislocation pile-ups at intragranular precipitates. The kink band formation relieved stress concentration induced by intragranular precipitates and promoted a ductile fracture. These exceptional outcomes provide opportunities for additive manufacturing RHEAs and greatly advance understanding of their strengthening and deformation mechanisms.

Additively manufactured Ni-20Cr to V functionally graded material: Computational predictions and experimental verification of phase formations

A database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and elemental V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing the amount of V across the gradient resulted in the formation of sigma phase, followed by the bcc phase. The experimentally measured phase data was compared with computational predictions made using a Cr-Ni-V thermodynamic database from the literature as well as the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations.

Enhanced localized corrosion resistance of Ni-based alloy 625 processed by directed energy deposition additive manufacturing

We report a significant improvement in localized corrosion resistance of Ni-based alloy 625 processed by directed energy deposition (DED) in 7.6 mol/L LiCl at 50 °C. Disparities observed in polarization behavior between DED and wrought alloy 625 are analyzed through compositional and microstructural differences. The exceptional localized corrosion resistance of the DED alloy likely stems from the refinement of secondary phases resultant from the DED process. The prototypical precipitates larger than ≈1 μm in the wrought material, primarily Nb,Ti-enriched face centered cubic nitrides with the stoichiometry of MN where M = Nb, Ti, Cr, served as pit initiation sites and likely degraded the corrosion resistance.

Pore evolution mechanisms during directed energy deposition additive manufacturing

Porosity in directed energy deposition (DED) deteriorates mechanical performances of components, limiting safety-critical applications. However, how pores arise and evolve in DED remains unclear. Here, we reveal pore evolution mechanisms during DED using in situ X-ray imaging and multi-physics modelling. We quantify five mechanisms contributing to pore formation, migration, pushing, growth, removal and entrapment: (i) bubbles from gas atomised powder enter the melt pool, and then migrate circularly or laterally; (ii) small bubbles can escape from the pool surface, or coalesce into larger bubbles, or be entrapped by solidification fronts; (iii) larger coalesced bubbles can remain in the pool for long periods, pushed by the solid/liquid interface; (iv) Marangoni surface shear flow overcomes buoyancy, keeping larger bubbles from popping out; and (v) once large bubbles reach critical sizes they escape from the pool surface or are trapped in DED tracks. These mechanisms can guide the development of pore minimisation strategies.

Microstructure and mechanical properties of a dilute Mg-Gd-Y-Zr alloy fabricated using wire-arc directed energy deposition additive manufacturing

Mg-Gd-Y-Zr is an important lightweight material in the aerospace field. However, the engineering applications of large-scale Mg-Gd-Y-Zr alloy components are significantly limited by macrosegregation and microstructure coarsening during the casting process. In recent years, wire-arc directed energy deposition (DED), which has high design and manufacturing freedom, provides a new route to manufacture large-scale metal parts. In this work, a dilute Mg-3.2Gd-0.6Y-0.5Zr (wt.%) alloy, which had a degree of alloying comparable to AZ31 commercial alloys, was fabricated using a wire-arc DED process based on tungsten inert gas (TIG) welding, the microstructure evolution, tensile properties, and impact toughness were investigated. Due to the multiple grain refinement effects, the DED sample displayed a fine-grain structure (12.3 ± 7.4 μm), which was comparable to that of the wrought counterpart. Combined with the ductilizing effects of the fine grain and the Gd/Y solutes, the DED sample achieved a high ductility, which was better than most reported additive-manufactured Mg alloys as well as wrought Mg alloys. The good plastic deformation capacity also endowed the DED sample with good crack propagation resistance under dynamic impact load. We hope this work can help guide the further development of high-performance Mg alloys that are specially designed for the wire-arc DED additive manufacturing process.

Yield strength evaluation of 3D-printed Ti–6Al–4V components based on non-contact eddy-current measurement

The demand for metal additive manufacturing (AM), also known as three-dimensional (3D) printing, is increasing owing to its ability to produce components with complex shapes, heterogeneous material properties, low material waste, etc. However, for broader acceptance of metal AM, quality assurance of mechanical properties, such as elastic (Young's) modulus, Poisson's ratio, and ductility, is becoming critical in additively manufactured components. This study proposes a non-contact, non-destructive and automated eddy-current technique for yield strength estimation of additively manufactured Ti–6Al–4V components with the potential for online operation during metal AM. After conducting the eddy-current measurement at a specific inspection point within the Ti–6Al–4V component, the relationship between the eddy-current phase value and electrical conductivity is established. Subsequently, the yield strength of the Ti–6Al–4V component is estimated by utilizing the analytical relationship between yield strength and electrical conductivity, which is based on Andrew's research and the Hall–Petch relationship. For experimental validation, Ti–6Al–4V test specimens with different electrical conductivities (yield strengths) were fabricated by adjusting the cooling rate during metal direct energy deposition AM. Then, the yield strengths estimated using the proposed technique were compared with those obtained using conventional destructive tensile tests. The results show that the proposed technique can estimate the yield strength with an error rate of less than 8%. The uniqueness of this study lies in (1) the theoretical derivation of the relationship between the eddy-current phase value and yield strength, (2) non-destructive, non-contact, and automated estimation of the yield strength using eddy-current measurements, and (3) performance evaluation using additively manufactured Ti–6Al–4V specimens with different yield strengths.

Enhanced Microstructure and Wear Resistance of Ti-6Al-4V Alloy with Vanadium Carbide Coating via Directed Energy Deposition.

Ti-6Al-4V alloys are known for their suboptimal tribological properties and are often challenged by durability issues under severe wear conditions. This study was conducted to enhance the alloy's wear resistance by forming a hardened surface layer. Utilizing directed energy deposition (DED) additive manufacturing with a diode laser, vanadium carbide particles were successfully integrated onto a Ti-6Al-4V substrate. This approach deviates from traditional surface enhancement techniques like surface hardening and cladding, as it employs DED additive manufacturing under parameters akin to those used in standard Ti-6Al-4V production. The formed vanadium carbide layer achieved a remarkable thickness of over 400 µm and a Vickers hardness surpassing 1500 HV. Pin-on-disk test results further corroborated the enhanced surface wear properties of the Ti-6Al-4V alloy following the additive-manufacturing process. These findings suggest that employing vanadium carbide additive manufacturing, under conditions similar to the conventional DED process with a diode laser, significantly improves the surface wear properties of Ti-6Al-4V in metal 3D-printing applications.

Vibration bending fatigue analysis of Ti‐6Al‐4V airfoil blades repaired using additive manufacturing

AbstractThe repair of airfoil blades is an enabling technology to extend the operational life of gas turbine engines. Directed energy deposition (DED) additive manufacturing (AM) provides the ability to add material by melting blown powder using a directed energy source. Seventeen Ti‐6Al‐4V airfoil blades were repaired using DED AM and analyzed to determine what effect the repair will have on the blade performance in high cycle vibration fatigue testing. Volumetric computed tomography (CT) was used to quantify the pores from the AM process. The blades were then subjected to vibrational bending fatigue, used to simulate turbine engine loading conditions, until failure. Only three blades failed in the repaired sections. The identified pore sizes and fatigue stress distribution in the blade were used to suggest that understanding the impact of internal and near surface level pores arising from the AM repair is critical towards the implementation of AM repair in aerospace components under fatigue loading.

A Sensing Integrated Metal Additive Manufacturing Platform for Exploring the Use of Non-Standard Powders

Abstract Research applications that rely on commercial directed energy deposition (DED) based metal additive manufacturing (AM) systems are commonly constrained by their inflexibility in handling various non-standard powders, lack of fine system control, and inherent difficulty with sensor integration. In this work, we present the design of a sensing-integrated platform for metal additive manufacturing. A modular design allows easy modification of specific sub-systems, such as laser integration or powder delivery mechanisms, to enable capabilities that are difficult to realize with commercial systems. As an example, we demonstrate DED performance using non-conventional inexpensive powders produced via abrasion and water atomization techniques. System performance is evaluated using integrated sensors and complemented by numerical/ analytical calculations. Based on these results, a nominal operation map combining thermal field with powder flow is generated for determining process parameters suitable for a given material/build combination and can be generally applicable for any DED AM system. In addition to handling non-spherical and alternatively sourced powders, the system capabilities for printing multi-material complex contours are demonstrated.

Corrosion response of steels fabricated through arc directed energy deposition additive manufacturing: a review.

Steels exhibit distinct properties that underscore their pivotal role in critical industries, such as maritime, aerospace, automotive, petrochemical, and biomedicine. In recent times, there has been an increasing trend towards manufacturing near-net-shape steel components through various additive manufacturing (AM) modalities, utilizing intricate 3D model data. Initially, powder bed fusion (PBF) technology garnered significant attention for the fabrication of steel components. Nonetheless, arc-directed energy deposition (arc-DED), also known as wire arc additive manufacturing (WAAM) technology, is progressively gaining prominence in the AM enterprise due to its high production rate, the ability to print large-scale components, and notably, reduced capital investment. While early research on WAAM-fabricated steels primarily focused on microstructural and mechanical characteristics, there is an increasing emphasis on the corrosion performance of WAAM steel components. These components often encounter exposure to corrosive environments in their intended applications. The existing literature lacks a comprehensive review that delves into the nuanced factors influencing the corrosion behavior of WAAM-fabricated steels and the primary corrosion mechanisms governing their degradation. Therefore, this review is dedicated to exploring the corrosion properties of WAAM-fabricated steels, identifying key parameters influencing their degradation behavior. Moreover, it offers an in-depth examination and discussion of the underlying mechanisms governing corrosion-induced deterioration. Furthermore, this review meticulously scrutinizes the microstructural features and WAAM technologies, providing clarity and organization regarding details relevant to the corrosion of WAAM steel components. To conclude, the paper highlights the existing research gaps related to the corrosion of WAAM steel, delineating potential avenues for future research.

A practical simulation approach for the support of wire arc DED additive manufacturing systems design

Wire Arc Directed Energy Deposition (DED) is a subcategory of the DED Additive Manufacturing (AM) technology that is rapidly gaining popularity due to its potential to produce large-scale, complex metal parts while maintaining a lower cost and higher productivity and sustainability when compared with other AM technologies. Additionally, Wire Arc DED permits a high level of customization in terms of designing and implementing manufacturing systems. However, there are still important quality issues hindering wire Arc DED, along with limited simulation approaches that offer the necessary practicality, from an industrial perspective, to support the design of wire Arc DED manufacturing systems. Towards this goal, aiming to the minimization of the experimental cost and time, a simulation approach of wire arc DED is presented in this study. The model has been developed in COMSOL Multiphysics, allowing for a high level of customization, while maintaining practicality and ease of use, both in terms of implementation as well as computational cost. The modeling approach is analyzed, followed by the implementation of the methodology in a case study. Preliminary results are presented, discussed, and accompanied by suggestions for future research.

Process monitoring by deep neural networks in directed energy deposition: CNN-based detection, segmentation, and statistical analysis of melt pools

The complex interaction between laser and material in Laser Wire Direct Energy Deposition (LW-DED) Additive Manufacturing (AM) benefits from process monitoring methods to ensure process stability and final part quality. Understanding the relationship between process parameters and melt pool geometrical characteristics can be used to effectively monitor and in-process control the process, as the melt pool geometrical characteristics serve as crucial indicators of process stability and quality.This study presents a novel in-situ monitoring approach for LW-DED, utilizing process images for melt pool segmentation, melt pool geometrical characteristics estimation, process stability assessment, and bead geometry prediction. The segmentation of melt pool objects was successfully accomplished using Convolutional Neural Networks (CNN)-based models, enabling the prediction of essential parameters such as melt pool area, height, width, center of area, and the center point of the bounding box enclosing the melt pool. Multiple models were compared regarding the accuracy and processing speed using a controlled central composite design and random experiments. We used an Inconel alloy 625 wire and two distinct substrate materials for deposition, a coaxial laser welding head with a 3 kW fiber laser, and an off-axis welding camera for monitoring.Among the CNN architectures evaluated, YOLOv8l demonstrated superior accuracy with mean Average Precision (mAP) values of 0.925 and 0.853 for Stainless Steel (SS) and low carbon steel (S355) substrates, respectively. Additionally, YOLOv8s exhibited a notable processing speed of over 114 frames per second, which indicates its suitability for real-time process control. Furthermore, the results indicate a significant correlation between process parameters and melt pool geometry variables. Notably, a clear correlation was established between melt pool characteristics and bead geometries obtained through microscopic examinations, including penetration depth and heat-affected zone. The analysis revealed a significant correlation for the bead area and width parameters. In relation to the bead height, while the correlation exhibited a lower magnitude compared to bead area and width, it remained responsive. In addition, the tensor masks derived from the developed models have proven to be highly effective in accurately predicting bead geometries.The results demonstrate the effectiveness of YOLO-based algorithms for detecting and segmenting the melt pool. Statistical analysis confirms the significance of stabilized process data and the accuracy of melt pool geometric models. We demonstrate that integrating advanced monitoring and control techniques using artificial intelligence methods like CNN can facilitate process stability and quality control.