Directed Energy Deposition Process Research Articles

Integrated top-down process and voxel-based microstructure modeling for Ti-6Al-4 V in laser wire direct energy deposition process

Laser-wire metal additive manufacturing (AM) is one of the ideal direct energy deposition (DED) processes for creating large-scale parts with a medium level of complexity. However, the DED process involves complex thermal signatures and wide length scales making the fabrication of realistic AM components and part qualification often reliant on experimental trial-and-error optimization. While experimental measurements over the full volume of a part are valuable and necessary, measuring the entire area of a part is significantly laborious and practically infeasible, particularly for large parts in terms of cost and rapid qualification. Therefore, in this work, we developed an effective thermal and microstructure modeling framework based on the Johnson–Mehl-Avrami-Kolmogorov (JMAK) and Koistinen & Marburger (KM) models through a top-down approach that considers plate distortion-affected thermal profiles. A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. The predicted results were validated through detailed metallurgical measurements. A combined voxel-by-voxel approach with a sparse data reconstruction technique produced a near-perfect reconstruction of the original data. This approach anticipates a significant reduction in data points and computation time and resources. Lastly, we conclude with potential extensions of this work to other modeling efforts.

Studies on hardness, elastic modulus and high temperature oxidation resistance of TiC and TiB2 dispersed titanium aluminide (Ti45Al5Nb0.5Si) composites developed by laser direct energy deposition

In this study, TiC (5 to 10wt.%) and TiB2 (5 to 10wt.%) reinforced TiAl (Ti45Al5Nb0.5Si) composites have been developed by laser direct energy deposition (LDED) process. The microstructures of TiAl/TiC and TiAl/TiB2 composites consist of TiC and TiB2 particles dispersed in γ and α2 matrix. The microhardness of the composites is improved from 571 HV to 634-683 HV (for TiC dispersion) and 649-694 HV (for TiB2 dispersion). The activation energy of cyclic oxidation is increased from 228kJ/mol (Ti45Al5Nb0.5Si) to 271-301kJ/mol (TiC dispersion) and 262-296kJ/mol (TiB2 additions). The mechanism of oxidation of the composites is established.

Machine Vision to Provide Quantitative Analysis of Meltpool Stability for a Coaxial Wire Directed Energy Deposition Process.

Wire-based additive manufacturing (AM) is at the forefront of complex metal fabrication because of its scalability for large components, potential for high deposition rates, and ease of use. A common goal of wire directed energy deposition (DED) is preserving a stable process throughout deposition. If too little energy is put into the deposition, the wire will stub into the substrate and begin oscillating, creating turbulence within the meltpool. If too much energy exists, the wire will overheat, causing surface tension to take over and create liquid drips as opposed to a solid bead. This paper proposes a computer vision technique to work as both a state detection and event detection system for wire stability. The model utilizes intensity variations along with frame-to-frame difference calculations to determine process stability. Because the proposed model does not rely on machine learning techniques, it is possible for an individual to interpret and adjust as they see fit. The first part of this paper describes creation and implementation of the model. The model's capability was then evaluated using a 1D laser power experiment, which generated a wide range of stability states across varying powers. The model's accuracy was evaluated through 3D geometry data gathered from the experimentally deposited beads. The model proved to be both capable and accurate and has potential to be used as a real-time control system with future work.

Prediction of catchment efficiency in direct energy deposition using dimensional analysis and machine learning

Direct Energy Deposition (DED) is an additive manufacturing process used to fabricate thin/thick-walled structures and high-value component restorations. As blown powder-based DED gains prominence, catchment efficiency becomes imperative for enhancing material utilization and process effectiveness. This study focuses on predicting catchment efficiency within a DED process by investigating four significant process parameters: laser power, powder feed rate, carrier gas flow rate, and deposition speed. The primary objective is to devise a dimensionless number capable of predicting catchment efficiency based on a combination of these parameters, categorizing efficiency into low, medium, and high regions. This approach reduces the need for resource-intensive experimentation. The experimental validation of the proposed dimensionless number was conducted, showing an error rate of around 8%. Additionally, six machine learning classifier algorithms – Support Vector Machines, K-Nearest Neighbours, Decision Tree, Random Forest, Linear Regression, and Gaussian Naive Bayes – were employed to categorize catchment efficiency zones. Support Vector Machine demonstrated the best performance with a predictive accuracy of 0.98. This study provides a methodology for catchment efficiency prediction, enhancing decision-making and resource optimization, and can be used to optimize process parameters for thin-wall or relatively thin-wall fabrication.

Characterization and simulation of AlSi10Mg reinforcement structures by direct energy deposition by means of laser beam and powder

Among metal additive manufacturing technologies, direct energy deposition (DED) processes have the advantage to be easily integrable in a manufacturing chain with other conventional technologies. This characteristic can be exploited by designing reinforcement structures to be added by DED onto pre-existing subcomponents to tailor the part’s mechanical properties while keeping the part lightweight. This study focuses on DED by means of laser beam and powder process optimization to improve material quality and geometrical accuracy of AlSi10Mg reinforcement structures while preventing excessive thermal deformations and material dilution into the substrate. These results are compared with finite elements numerical simulations of the deposition process, comprising thermo-elastic deformation and material deposition, to predict the bending and reinforcement of the processed substrate. In particular, the model includes the deterministic prediction of the deposition profile as a function of the process parameters and a few condition-specific coefficients: once calibrated, the model was used to compare the numerical and experimental residual deformation of the reinforced sample, obtaining promising agreement. The reinforcement provided to a 1.5 mm thick substrate by a single wall of deposited materials, with cross-sectional dimensions of 2 mm in width and 2.5 mm in height, was evaluated by three points bending. With the reinforcement on the tensile side of the stresses, the energy absorbed by the material plastic deformation increased by 2.4% as compared to the substrate alone, while with the reinforcement on the compression side of the stresses the energy absorption increased by 75.8% on average.

Life cycle assessment in additive manufacturing of copper alloys—comparison between laser and electron beam

AbstractAdditive manufacturing is becoming increasingly important for industrial production. In this context, directed energy deposition processes are in demand to achieve high deposition rates. In addition to the well-known laser-based processes, the electron beam has also reached industrial market maturity. The wire electron beam additive manufacturing offers advantages in the processing of copper materials, for example. In the literature, the higher energy efficiency and the resulting improvement in the carbon footprint of the electron beam are highlighted. However, there is a lack of practical studies with measurement data to quantify the potential of the technology. In this work, a comparative life cycle assessment between wire electron beam additive manufacturing (DED-EB) and laser powder additive manufacturing (DED-LB) is carried out. This involves determining the resources for manufacturing, producing a test component using both processes, and measuring the entire energy consumption. The environmental impact is then estimated using the factors global warming potential (GWP100), photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). It can be seen that wire electron beam additive manufacturing is characterized by a significantly lower energy requirement. In addition, the use of wire ensures greater resource efficiency, which leads to overall better life cycle assessment results.

Enabling tailored microstructures by hybrid directed energy deposition processing of a nickel-based superalloy

The technological advances in additive manufacturing, particularly laser based directed energy deposition (DED), revolutionized the production of complex metal components. Despite this progress, the oriented heat flux and several reheating thermal cycles can induce a strongly textured microstructure, which induces an anisotropic mechanical behavior. In addition, considerable residual stresses typically require additional post-processing. Therefore, hybrid process chains for additive manufacturing (AM) are being developed, which aim at integrating conventional post-processing into the AM process. However, a detailed investigation of thermal and mechanical effects of such hybrid processes on the mechanical properties and their interrelation is lacking. In an experimental study, we explore the integration of thermal and mechanical processing steps within the DED process chain to locally tailor microstructures and mechanical properties. Through electron backscatter diffraction measurements, we demonstrate significant microstructural changes of DED-manufactured nickel-based superalloy samples using deep rolling and laser heat treatment. A mechanical surface deformation induces microstructural misorientation leading to an increase in hardness down to substantial depth of several hundred micrometers. Additionally, the targeted management of heat input during laser heat treatment results in different grain morphologies and sizes, affecting average microhardness within a significant depth. The results demonstrate the potential for microstructural tailoring using hybrid AM process chains, while a substantial sensitivity of the microstructure to thermal and mechanical load emphasizes the importance of a precise process control. This work provides an understanding of the process-microstructure-property relationship required for developing new process pathways in hybrid AM that integrate thermal and mechanical processes into DED manufacturing.

First step toward designing effective real-time control systems in laser directed energy deposition

In-process monitoring and control are essential for quality assurance and consistency of laser-based directed energy deposition processes. Detection of irregularities during deposition in terms of defects or flaws is based on in situ monitoring of output process parameters such as temperature, melt-pool geometry, or deposition height. The real-time feedback of these output parameters allows the development of control strategies for real-time adjustment of input process parameters, such as laser power or scanning speed, to correct detected deviations from the desired output process parameters. Therefore, criteria such as sensitivity, stability, correlation, trends, and interactions of the input-output process parameters have a direct impact on controller design, establishing, for example, control limits or tolerance ranges of the output parameters. This paper focuses on the study of the characteristics of output process parameters to input process parameters. This research involves analyzing and comparing the deposition of single tracks under various input process parameters, including laser power and scanning speed. Melt-pool geometry and temperature are estimated from a visual camera and a hyperspectral line camera, whereas the final deposition geometry is obtained from a laser triangulation scanner. The results show the linearity between input and output process parameters, the steadiness of the output process parameters, the relation between melt-pool and final deposition, and offer insights to design effective in-process control systems.

Influence of robot-guide strategies on the geometry of directed energy deposition components

Directed energy deposition (DED) for additive manufacturing applications is commonly realized by the usage of industrial robots. The DED processing end effectors are usually mounted on industrial robot systems and can, therefore, be moved and oriented in up to many degrees of freedom. However, the design of the powder nozzle and the programming of the robot can limit the movement options. For this reason, translational movements, as with conventional 3D printers, are still common today. The end effector is usually guided horizontal over the component surface although welding in position (PA) is preferred in general. It may be necessary to tilt the end effector in order to gain advantages during processing due to the constrained position. This is particularly advantageous for overhang structures and is often realized with the help of a turn-tilt positioning table in combination with an industrial robot. However, this approach is, in some cases, not possible due to geometrical constrains. To extend applications in this direction, advanced methods for slicing the components and programming the robot movements are necessary. The main aspect of this work is the development, testing, and evaluation of a multidimensional DED manufacturing approach. This is tested on a thin-walled component with defined overhang areas and compared with conventional approaches. Different strategies are assessed in terms of the geometrical match of the target geometry. Influences of strategies on the results are evaluated. It can be shown that multidimensional path planning approaches lead to a better match of the target geometry.

Influence of Printing Parameters on the Morphological Characteristics of Plasma Directed Energy-Deposited Stainless Steel

This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing.

An integrated hybrid wire-arc directed energy deposition, friction stir processing, and milling system for multi-track, multi-layer part manufacturing

Wire-based Directed Energy Deposition (DED) is a widely-used manufacturing method due to its high productivity and large part fabrication capability. Meanwhile, Friction Stir Processing (FSP) is a solid-state joining process that can modify microstructure and weld lightweight alloys. Additionally, wire-based DED printed parts need machining process to achieve the desired dimensional accuracy. To take advantage of all these three processes, this work proposes an integrated hybrid system by combining the wire-arc DED, FSP, and milling processes into a standalone system which can fabricate superior materials in a multi-track, multi-layer manner for the first time. The integrated system can improve dimensional accuracy and productivity by processing the workpiece without the need to move it between different systems. It is demonstrated that a 150 × 40 × 21 mm3 block of aluminum alloy AA5183 can be fabricated using the hybrid wire-DED/FSP/milling process from wire feedstock. Material characterization shows that the hybrid process is able to refine the grain size by two orders of magnitude to sub-micron scale, while eliminating all the pores and microcracks produced by the DED process. These enhancements result in significantly improved mechanical properties including Young's modulus (15 %), yield strength (161 %), ultimate strength (33 %), and hardness (55 %) without compromising ductility.

An Investigation of Thermomechanical Behavior in Laser Hot Wire Directed Energy Deposition of NAB: Finite Element Analysis and Experimental Validation

Laser Hot Wire (LHW) Directed Energy Deposition (DED) Additive Manufacturing (AM) processes are capable of manufacturing parts with a high deposition rate. There is a growing research interest in replacing large cast Nickel Aluminum Bronze (NAB) components using LHW DED processes for maritime applications. Understanding thermomechanical behavior during LHW DED of NAB is a critical step towards the production of high-quality NAB parts with desired performance and properties. In this paper, finite element simulations are first used to predict the thermomechanical time histories during LHW DED of NAB test coupons with an increasing geometric complexity, including single-layer and multilayer depositions. Simulation results are experimentally validated through in situ measurements of temperatures at multiple locations in the substrate as well as displacement at the free end of the substrate during and immediately following the deposition process. The results in this paper demonstrate that the finite element predictions have good agreement with the experimental measurements of both temperature and distortion history. The maximum prediction error for temperature is 5% for single-layer samples and 6% for multilayer samples, while the distortion prediction error is about 12% for single-layer samples and less than 4% for multilayer samples. In addition, this study shows the effectiveness of including a stress relaxation temperature at 500 °C during FE modeling to allow for better prediction of the low cross-layer accumulation of distortion in multilayer deposition of NAB.

Evaluation of Numerical and Analytical Methods to Accurately Measure Elastic Modulus of the Cobalt Chromium Alloy Fabricated by Laser Direct Energy Deposition Process

Evaluation of Numerical and Analytical Methods to Accurately Measure Elastic Modulus of the Cobalt Chromium Alloy Fabricated by Laser Direct Energy Deposition Process

Deep learning-based rapid prediction of temperature field and intelligent control of molten pool during directed energy deposition process

In this work, deep learning-based approaches are proposed to provide promising solutions to address the challenges in realizing intelligent manufacturing and digital twins for directed energy deposition (DED) process. Firstly, a rapid and accurate prediction of part temperature is realized by innovatively combining graph neural networks (GNNs) and recurrent neural networks (RNNs). Twenty parts with different structures are selected for demonstration. GPU parallel computing technique is adopted to accelerate the thermal finite element analysis, which is used to quickly construct the simulated graph dataset with sufficient samples. By embedding the memory optimization method into the GNN block, deeper GNNs with more trainable parameters are successfully trained with a 79.4% lower GPU memory footprint, which solves the difficulty of deeper GNNs are hard to train on large graph datasets, and the accuracy of temperature prediction on unseen DED parts is significantly improved. Secondly, for intelligent molten pool regulation, a semi-analytic temperature solution method is used to create an efficient DED environment in reinforcement learning (RL) workflows. The intelligent control of molten pool depth under complex deposition strategy is realized based on the environmental state represented by molten pool images. A tailored convolutional neural networks (CNNs) model is employed as the agent to output varying laser power and continuously interact with the dynamic environment. Compared with the traditional artificial neural network agent, the total reward scored by the CNN agent is improved by 9.7% in the zigzag deposition process, mitigating the fluctuations in the controlled molten pool depths. Moreover, CNNs are more compatible with in-situ thermal images. This work can provide theoretical and technical support for realizing real-time and even ahead-of-time temperature prediction and the corresponding feedback control during DED process.

Directed energy deposition of Ti6Al4V wire with continuous layer varied laser power

The laser-based directed energy deposition (DED) additive manufacturing enables the efficient fabrication of metallic parts. The layer-wise performance in DED-built metallic parts is challenging to remain consistent due to the intricate heat accumulation. As such, it is of significance to control the printing quality by adjusting the process settings with layers. In this work, we used DED to achieve Ti6Al4V wire deposition with continuously layer-varied laser power. Three laser power settings were selected to investigate the variation in defects, microstructure, and mechanical properties with layers. Our findings suggested that optimizing real-time laser power is critical for achieving desirable manufacturing efficiency and resultant performance in the DED of metallic materials. This research provides valuable guidelines for parameter optimization in DED processes, aiming to enhance the production quality and performance of high-strength metallic components, particularly in sectors demanding high-performance materials.

Influence of milling interventions on the geometry of wall-shaped structures in hybrid wire-arc direct energy deposition

The Hybrid Wire-Arc Direct Energy Deposition (Hybrid Wire-Arc DED) process integrates Wire-Arc Direct Energy Deposition (Wire-Arc DED) with machining (typically milling) interventions, offering the potential for creating intricate geometries and finished surfaces. However, if milling is employed as a hybrid intervention rather than as a final part-finishing process, the interplay between these processes remains under-investigated. This paper examines the influence of milling interventions on the geometry of a wall-shaped structure, quantified by the transverse cross-sectional width, built using a Hybrid Wire-Arc DED. Through experiments on mild steel, the underlying causes of observed wall-width variations are analyzed. Initial observations suggested that thermo-mechanical deformations from milling influence the width variations. However, evidence indicates the significant role of additional remelting cycles experienced by the milled surface layer during subsequent layer depositions. The study also reveals that the observed increase in wall width for each milling intervention occurs at approximately the same depth below the milled surface. A mechanistic explanation for this observation is given. Crucially, the findings suggest that unless milling is done at higher frequencies, like after each layer deposition, the resultant unevenness might render the Hybrid Wire-Arc DED process less efficient in terms of surface quality and dimensional accuracy than its non-hybrid counterpart.

Computational modeling of multi-track multi-layer laser directed energy deposition process

This study presents the development of a 3D fully coupled thermomechanical finite element model to evaluate the melt pool geometry, defects, and micro-hardness of multi-track multi-layer deposition in the powder feed laser-directed energy deposition (LDED) technique. Fabrication strategies can significantly affect the melt pool geometry and material behavior of the manufactured components. Understanding the effect of process parameters in a multi-track multi-layer LDED is important. Optimal process parameters are crucial for achieving high-quality deposition in terms of deposition geometry and integrity. The model can aid in mitigating issues such as inadequate inter-track or track-substrate fusion, porosity, cracks, and other defects. A multi-track multi-layer finite element model (FEM) can realistically predict melt pool evolution, mechanical properties, and defects in laser-LDED. This work focuses on the development of a fully coupled thermomechanical model for multi-track multi-layer deposition processes using ABAQUS®. The finite element framework employs Johnson-Cook’s flow stress model for constitutive behavior and the Koistein-Marburger equation for predicting hardness. Two tracks and two layers of SS316L powder are deposited on an SS304 substrate in finite element simulations and experiments. The model has been validated for the melt-pool depth in the substrate, deposition geometry, heat-affected zone, and hardness. It has been observed that there is a ∼10 % error in the prediction of melt pool geometry characterization and a ∼12 % error in hardness as compared to experimental results. The melt-pool width, depth, substrate dilution, and hardness have been characterized as a function of process parameters via simulations and experiments. The model is capable of predicting inter-track and interface defects. In addition, the effect of interlayer cooling on the melt-pool geometry and hardness has been studied.

Effect of Deposit Scale on Mechanical Properties of In-Situ Alloyed CrCoNi Medium Entropy Alloys Formed by Directed Energy Deposition

Directed energy deposition (DED), as an additive manufacturing technology, has shown unique advantages in multi-material additive manufacturing and remanufacturing. In this study, two types in-situ alloyed CrCoNi medium entropy alloys that have thin-walled structures with different thicknesses (T1 and T2) were manufactured by the DED process, and the mechanisms of differences in relative density, microstructure, and mechanical properties at different heights were systematically analyzed. In terms of microstructure, the T1 and T2 samples along the building direction exhibit significant differences in crystallographic orientation, grain size, and dislocation density, which are related to the local temperature gradient differences caused by the scanning path and heat accumulation. In terms of mechanical properties at different heights of the two types of thin-walled structures, the yield strength is higher but the elongation is lower at the bottom position of sample, while the yield strength is lower but the elongation is higher at the middle and top positions. The differences of mechanical properties at different heights of the T1 and T2 samples are related to the microstructure and relative density. This finding provides new insights for the design and performance analysis of complex thin-walled structures formed by additive manufacturing.

Achieving low-porosity AlSi10Mg cladding layers for the additive repair of aluminum alloy parts by directed energy deposition

Directed Energy Deposition (DED) has broad application prospects for repairing damaged aluminum alloy parts such as piston aero-engine casing. However, due to the low laser absorptivity and the complex thermal cycle process, it is challenging to obtain aluminum alloy cladding layers with high-quality morphology and low-porosity by DED method. In this work, a series of individual stages of DED process are studied during single-tracks overlapping and layers stacking. Correspondingly, the within layer scanning strategy, inter-layer stacking strategy, and process parameters are examined carefully. Using the proposed DED processing flow that combines substrate preheating with intermittent process, the deposited layers without balling and collapse are successfully achieved. Furtherly, the bi-directional scanning strategy for single-tracks overlapping within layer is proposed to compensate the thickness nonuniformity caused by heat accumulation. The inter defects identified as “lack of fusion” and “gas pores” are observed in the cladding layers with uniform surface. By analyzing the pore formation mechanism and considering the energy distribution and re-melting, the multi-layer offset stacking strategy is proposed to inhibit the porosity to 0.25 %. Combined with the optimized process parameters, the porosity is reduced up to 0.09 %, ensuring the density of the DED deposition coating. This work provides new pathway and direct guidance for fabricating high-quality AlSi10Mg cladding layers during the DED repairing of valuable aluminum alloy parts.

Integrated framework of multi−physics mechanism models for gas−powder flow, molten pool evolution and part deformation in high alloy steel additive manufacturing process

Multi−physics numerical models for metal additive manufacturing (MAM) could replace the expensive trial−and−error experiments, reveal the mechanisms of printing process, and provide an effective means of optimizing process parameters and mitigating part defects. However, the existing simulations of MAM process seldom consider the coupling effects between multiple recursive processes. There is an urgent need to overcome the dilemma of limiting MAM process simulation to a localized single sub−process. For directed energy deposition (DED) additive manufacturing process, the integrated simulation framework proposed in this study consists of a gas−powder flow model, a heat and fluid flow model, and a sequentially coupled thermal–mechanical model, which can be used to predict the feedstock feeding process, the evolution of molten pool, and the residual deformations of metal part, respectively. The accurate results of the three recursive sub−processes can be obtained by delivering the validated parameters between models. Corresponding experiments are conducted to verify the accuracy of the integrated framework based on 12CrNi2 alloy powders. The deviation of the predicted focal distance of powder streams is 5.14 %, and the deviations of the predicted molten pool height and width are all less than 8.5 %. The integrated framework could rapidly and accurately predict the residual deformation tendency and the position where the maximum deformation occurs. For single−track and eight−layer line deposition, the deviations of the predicted maximum deformation are all within 0.1 mm. The integrated framework could efficiently provide accurate and comprehensive solutions for optimizing DED process.