Search

The properties and microstructure of Type 304L stainless steel produced by two additive manufacturing (AM) methods—directed energy deposition (DED) and powder bed fusion (PBF)—are evaluated and compared. Localized heating and steep temperature gradients of AM processes lead to significant residual stress and distinctive microstructures, which may be process-specific and influence mechanical behavior. Test data show that materials produced by DED and PDF have small differences in tensile strengths but clear differences in residual stress and microstructural features. Measured fatigue crack growth rates (FCGRs) for cracks propagating parallel to and perpendicular to the build directions differ between the two AM materials. To separate the influences of residual stress and microstructure, K-control test procedures with decreasing and constant stress intensity factor ranges are used to measure FCGRs in the near-threshold regime (crack growth rates ≤ 1 × 10−8 m/cycle). Residual stress is quantified by the residual stress intensity factor, Kres, measured by the online crack compliance method. Correcting the FCGR data for differences in Kres brings results for specimens of the two AM materials into agreement with each other and with results for wrought specimens, when the latter are corrected for crack closure. Differences in microstructure and tensile strength have an insignificant influence on FCGRs in these tests.

The sequential digital design and manufacturing of components play a crucial role in realizing the industrial potential of directed energy deposition (DED), particularly when employing an electric arc as the energy source to melt a filler wire (DED-ARC). This study explores the application of DED-ARC for manufacturing large-scale, load-bearing structures, using a railway bogie as a case study. Originally a cast Bettendorf-type design, the bogie was redesigned using a multi-material approach. High-strength low-alloy (HSLA) steel was utilized in high-stress areas, while low-carbon steel was used elsewhere to reduce mass, enhance manufacturability, and improve repairability. The workflow included computer-aided design (CAD), topological optimization, finite element analysis (FEA), material selection, and iterative CAD modifications to address process constraints. The redesigned bogie underwent pre-manufacturing, fabrication, and a final scan of the as-built part. Representative multi-material wall samples were characterized, revealing typical microstructures and elastic limits of 468 MPa and 737 MPa for ER70S-6 and ER100S-G, respectively. These tensile properties were incorporated into FEA verification simulations, demonstrating a higher safety factor compared to the original design. A CAD-to-part analysis, including scan comparisons, highlighted manufacturing-induced deformation, material-dependent over-thickness, and localized geometric variations. This study offers a comprehensive overview of the DED-ARC process, from design through characterization, and demonstrates its capability to produce high-quality industrial components. The findings underscore the manufacturability and potential of DED-ARC for fabricating robust, multi-material structures for demanding applications. [ABSTRACT FROM AUTHOR]

The present work proposes the control of the interpass temperature and the use of arc oscillation of Ti6Al4V (grade 5) alloy in wire arc additive manufacturing (WAAM) depositions to predict and control mechanical properties and geometric uniformity. The results showed that by controlling the interpass temperature and using arc oscillation, satisfactory titanium depositions can be obtained without a closed chamber or the need for heat treatment. Monitoring the interpass temperature allows control of the mechanical properties and geometrical uniformity. However, no significant benefit in terms of mechanical properties or geometric uniformity was found by maintaining the interpass temperature below 120°C under the studied conditions. Using arc oscillation reduced the formation of columnar grains growing across the layers, decreasing the growth of prior-β grains in the vertical building direction. The interpass temperature control between 120°C and 400°C presented the most interesting results for the conditions studied, considering the trade-off between properties and productivity. [ABSTRACT FROM AUTHOR]

The current manuscript undertakes a systematic analysis of sequential layer-by-layer laser remelting (SLLR) within the context of laser-directed energy deposition (LDED) of Hastelloy-X (HX) bulk structures. Comparative analysis is conducted between the surface and bulk properties of LDED-built samples and those incorporating SLLR. Integrating SLLR with LDED results in a notable decrease in surface roughness by 71.5% and porosity by eight times. While both lack of fusion and gas porosity are evident in the as-built sample, the combination of SLLR with LDED shows only gas porosity. Moreover, microstructural refinement is observed after SLLR without preferential growth along (100), unlike in samples without SLLR. Analysis reveals segregations of Mo, Si, and C and the presence of Mo-rich carbides in both LDED and SLLR samples. The finer dendritic microstructures observed in SLLR samples contribute to a 12% increase in microhardness and a 7% rise in yield strength along the build direction compared to samples without SLLR. This study lays the path for fabricating dense components with tailored microstructures and mechanical properties during LDED through the utilisation of SLLR. [ABSTRACT FROM AUTHOR]

To investigate the impact of process parameters on the deposition morphology of AlMo0.5NbTa0.5TiZr refractory high-entropy alloy during laser directional energy deposition, mathematical models were constructed for laser power, scanning rate, powder feeding rate and dilution rate, aspect ratio, and deposition area. These models were developed using the response surface method, and multi-objective optimization was conducted using the NSGA-II genetic algorithm. The results demonstrate that the optimal process parameters are laser power of 1150.08 W, scanning rate of 436.23 mm/s, and powder feeding rate of 2.81 g/min. The errors between the actual and predicted results for the dilution rate, aspect ratio, and deposition area are 6.5, 7.25, and 6.45%, respectively. These findings validate the efficacy of the multi-objective optimization of NSGA-II and the model's reliability. The findings of this study provide a theoretical framework for predicting and controlling the morphology of the deposited layer of refractory high-entropy alloy. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes. [ABSTRACT FROM AUTHOR]

The direct energy deposition arc process is widely used for fabricating medium and large components with moderate geometric complexity but often results in coarse microstructures and inconsistent hardness. This study introduces a hybrid manufacturing approach combining the friction stir burnishing process with the direct energy deposition arc by a gas–metal arc welding technique to refine the microstructure and enhance the microhardness of components fabricated from austenitic stainless steel 316L. Our former study used an aluminum alloy (A5052) friction stir burnishing tool, demonstrating significant microhardness improvement and grain refinement. However, it also faced notable challenges under high-heat and -friction conditions, including the effect of material adherence to the workpiece during processing. Therefore, this study introduces a newly developed friction stir burnishing tool made from copper (C1100) and compares its performance with the aluminum alloy tool regarding microhardness enhancement and microstructure refinement. The results indicate that the specimen processed by direct energy deposition arc combined with the copper friction stir burnishing tool demonstrated the best overall performance in grain refinement and hardness enhancement. Specifically, it achieved the highest average microhardness of 250 HV at 50 µm depths, compared to 240 HV for the aluminum alloy tool. The statistical analysis showed that both tools led to significant improvements over specimens processed without them. The statistical analysis confirmed a notable reduction in secondary dendrite arm spacing across all depths, with the copper tool demonstrating the most refinement. Additionally, a preliminary investigation of corrosion behavior revealed tool-dependent differences. Overall, this study offers a promising approach to improving additive manufacturing, particularly for industries with less stringent surface finish requirements. It could potentially reduce post-processing time and cost. Future research should explore different process parameters and assess long-term corrosion performance to develop this hybrid technique further. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a promising solution for real-time monitoring of the quality of additively manufactured (AM) metallic parts. This study focuses on the Laser-based Directed Energy Deposition (L-DED) process and utilizes embedded vision systems to capture critical melt pool characteristics for continuous monitoring. Two self-learning frameworks based on Convolutional Neural Networks and Transformer architecture are applied to process zone images from different DED process regimes, enabling in-situ monitoring without ground truth information. The evaluation is based on a dataset of process zone images obtained during the deposition of titanium powder (Cp-Ti, grade 1), forming a cube geometry using four laser regimes. By training and evaluating the Deep Learning (DL) algorithms using a co-axially mounted Charged Couple Device (CCD) camera within the process zone, the down-sampled representations of process zone images are effectively used with conventional classifiers for L-DED process monitoring. The high classification accuracies achieved validate the feasibility and efficacy of self-learning strategies in real-time quality assessment of AM. This study highlights the potential of AI-based monitoring systems and self-learning algorithms in quantifying the quality of AM metallic parts during fabrication. The integration of embedded vision systems and self-learning algorithms presents a novel contribution, particularly in the context of the L-DED process. The findings open avenues for further research and development in AM process monitoring, emphasizing the importance of self-supervised in situ monitoring techniques in ensuring part quality during fabrication. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing is a significant and advancing manufacturing process on a worldwide scale. Wire + arc additive manufacturing (WAAM) is a progressed and efficient technique for producing large-scale near net shaped products by adding layers of material. This study presents pulsed current WAAM of a Hastelloy C-276 thin-wall component. The thin wall's metallurgical and mechanical properties were extensively investigated. This included examining samples from different travel and build orientations. The microstructures in different areas include of columnar, cellular, and equiaxed dendrites. The temperature distribution and rate of cooling may impact the structure of the layers. The scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDS) investigation showed a high Ni content and low Mo and W in the dendritic core region (DCR). SEM and EDS examines on several areas showed no cracking in the thin wall's travel and build orientations. In addition, the electron backscattered diffraction (EBSD) investigation showed that the average grain size was 66.38 μm in the (x-y) plane and 113.18 μm in the (y-z) plane. Reheating and solidification during layer-by-layer deposition altered grain characteristics. The hardness measurements exhibited variability across several locations. The existence of a well-defined directed dendritic microstructure, coupled with the presence of precipitates, provides corroborating evidence. The material has a maximum average ultimate tensile strength of 786 ± 6.1 MPa and elongation of 65.3 ± 3%. The fracture features are primarily ductile with periodic transgranular and intergranular behaviour. The pulsed current arc-based WAAM process offers a new and innovative method of depositing Hastelloy C-276. This method is applicable in chemical, nuclear, marine, and industrial sectors. [ABSTRACT FROM AUTHOR]

Wire-based directed energy deposition (DED) techniques have emerged offering the ability to manufacture large-scale metal components in the additive manufacturing landscape. Wire-based DED processes offer high deposition rates, material use efficiency, and suitability for a wide range of metals and alloys. On the other hand, these processes are limited in terms of dimensional accuracy of deposition, surface quality, and ability to produce highly complex geometry. Therefore, this review aims to provide a comprehensive analysis of recent advancements in wire-arc and wire-laser DED processes—with a specific focus on widely used titanium and aluminium alloys for aerospace applications with low buy-to-fly (BTF) ratio. It provides an overall understanding of wire-based DED processes and guides the reader with the choice to make appropriate process selection. Moreover, the study provides a discussion on the process intricacies, encompassing an overview of wire-based DED equipment and process capabilities, as well as an examination of critical process parameters. A detailed comparison is provided between wire-arc and wire-laser DED processes, covering essential aspects like path planning, deposition strategy, melt pool behaviour, obtained microstructures and mechanical properties, part quality, and defect mitigation. Additionally, post-processing techniques tailored for end-use applications are reviewed providing insights for achieving optimal results. The discussion extends to process monitoring and control, emphasising in situ techniques and process modelling for enhanced precision and efficiency. Finally, recommendations and highlights on potential areas for innovation and development as the future work are provided. [ABSTRACT FROM AUTHOR]

Alloys developed by fusion-based additive manufacturing often suffer from the coarse columnar grain structure and their effect on properties. This work involves the practical application of pulsed laser in laser-based directed energy deposition (DED-LB) of super duplex stainless steel which led to engineering the microstructure, improving the mechanical properties, and changing the dominant texture. Pulsed laser DED-LB (here P-DED) with laser spot sizes of 1 and 2 mm and different frequencies were used. Refine-grained ferritic steels containing porosity were produced when using a small laser spot size. Ferrite-to-austenite (α → γ) transformation was constrained to the grain boundaries under the effect of small excitation overlaps. Using broader laser, higher energy input, and ultra-short pulse intervals encouraged γ nucleation, promoted the density, and decreased the content of undesirable oxides that are typically formed during the conventional DED-LB (here C-DED). The local ferritization under the fusion lines of C-DED was avoided by P-DED. Directionally solidified α, extending into several layers, was inhibited by P-DED with optimum overlap. Enhanced supercooling resulted in an in situ grain refinement and columnar-to-equiaxed morphological transition. Defect-free microstructure and effective distribution of interphase boundary surface by P-DED, with a laser spot size of 2 mm and ⁓99% excitation overlap, largely improved the toughness and elongation (with acceptable strength). Pulse-induced convection and isotropic heat flow during P-DED with smaller laser spot size subsided the trend of preferred orientation. However, an alignment of < 001 > α with deposition direction during P-DED with the broader laser preserved the typical {001} < 100 > solidification texture and, consequently, the transformation texture. [ABSTRACT FROM AUTHOR]

The utilization of metal Additive Manufacturing (AM) has led to substantial progress in the manufacturing process of metal components. Evaluating the influence of the wide variety of factors related to the material type, AM process, and the resultant microstructure and properties is not easy and accurate by traditional engineering strategies. Therefore, one of the most effective ways to improve AM performance is to employ artificial intelligence methods, such as Machine Learning (ML), to establish complex links and enhance control over systems and product quality. The integration of these powerful ML techniques presents an emerging opportunity to revolutionize manufacturing processes, tackle production challenges, and optimize resource consumption. Consequently, in this review, the role of machine learning in laser-based metal additive manufacturing is explored by highlighting its applications in process optimization and property prediction. Evaluation of the results reported in the literature indicates that it is possible to establish relationships between process, structure, and properties by considering inputs such as part geometry, material properties, microstructural characteristics, and AM process parameters and utilizing ML algorithms. Defect detection and in-situ monitoring are among the other applications of ML algorithms in AM procedures, enhancing the manufacturability and repeatability of metal components. For this purpose, various linkages and correlations for Directed Energy Deposition and Laser Powder Bed Fusion are outlined in this review. The advancement in hardware and software will boost the advantage of applying data-driven approaches to overcome the obstacles in metal AM. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) of large metal components has clear potential to revolutionise supply chains in several sectors, including marine & offshore and oil & gas. To insert this technology in production, ensuring part quality and consistency is of primary importance. However, such insertion is hindered by bottleneck issues arising from the manufacturing process, including part distortion and non-uniform mechanical properties. To address this important industrial need, an integrated thermo-metallurgical-mechanical numerical model capable of directly reading the robot tool-path (g-code) as well as the component shape, is here developed in-house and tailored to steel EH36, which is of particular relevance to the marine, offshore and oil & gas sectors. The model computes temperature at part scale, microstructure (phase fraction distribution) and residual stress and distortion, where each step of the chain is informed by the previous one. After demonstrating the framework on a single bead and thin wall geometries, the framework is applied to investigate the role of tool path in printing a more complex geometry. The presented framework allows to digitally correlate part design, process parameters, and tool path with microstructure distribution, residual stresses, and distortion, supporting digital process development in DED of large metal components. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has evolved to enable the direct production of functional components through the hybridization of additive and subtractive processes. In metal wire AM, hybridization is key, encompassing process integration (addition/subtraction), energy source combinations (arc/laser), kinematic options (3/4/5 axis), and slicing techniques (planar/conformal). This paper focuses on these hybridization methods, with a unified system designed for single-machine setups, improving efficiency and accuracy. This study presents a detailed exploration of these hybridization levels through the fabrication of a complex 5-axis geometry—an impeller. The impeller was manufactured with hybridization using various levels and subsequently compared with manufacturing processes like additive manufacturing with interlayer machining and traditional machining methods. The hybrid approach significantly reduced the manufacturing time for the selected impeller geometry from 3536 min to 792 min (saving 77.6% manufacturing time) and minimized material waste to 9.3%, compared with 74.07% in traditional machining. This demonstrates a more efficient, precise, and cost-effective method to optimize metal wire AM for producing complex metal components, advancing capabilities and applications. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is one of the most promising additive manufacturing technologies, particularly for partial coating, repairing, and dissimilar metal depositing. However, determining the optimal deposition parameters to achieve desired shapes and mechanical properties is challenging because of the complex process of repeated melting and solidification driven by thermal energy. While some studies have focused on the cooling rate and its impact on mechanical properties, they have not evaluated the range within which the cooling rates vary. In this study, we investigated a range of cooling rates and their influence on hardness in DED under practical conditions using austenitic stainless steel SUS316L. The primary objective was to control the hardness of the deposited part by adjusting the cooling rate. Among the factors influencing hardness, grain size is particularly affected by cooling rates. Therefore, this study focused on grain size to evaluate the effects of DED technology. We proposed a method to calculate the cooling rate based on temperature distribution in the melt pool, captured by a camera coaxial with the laser beam. We varied the interpass dwell time, the resting interval between deposition layers, and investigated its effects on cooling rate and hardness. Additionally, the surface temperature of the workpiece during deposition was measured with a thermal camera. Results showed that while the dwell time did not significantly affect the calculated micro cooling rate, the deposition height had a notable impact. Conversely, the macroscopic temperature change was influenced by the dwell time. The cooling rate ranged from 3×103°C/s to 10×103°C/s, with hardness varying between 160 HV and 220 HV, and a linear correlation was observed between them. [ABSTRACT FROM AUTHOR]

Since its conception in the early 1990s, additive manufacturing of metallic materials has evolved into a staple manufacturing technique in various industries. Among other process-specific advantages, such as sustainability and geometric freedom, the unique thermal conditions of extremely fast heating and cooling cycles can allow for the manufacturing of alloy systems facing challenges in conventional processing or eliminate the need for further heat treatments. This holds true for Fe–Mn–Al–C steels, which exhibit extraordinary strength and ductility due to κ-carbide precipitation but encounter challenges during casting and demand extensive heat treatments. In this study, we set out to achieve in situ κ-carbide precipitation within an X110MnAl30-8 steel by manipulating the thermal history of the laser-based directed energy deposition process. Using CALPHAD calculations to determine suitable heat treatment temperatures, a preheating setup was developed, which allowed for the precise adjustment and measurement of the temperature development within the samples. Subsequently, different preheating temperatures were evaluated for potential in situ κ-carbide precipitation. Preheating the substrate plate to 500 °C resulted in κ-carbide formation in the as-built material. Simultaneously, the formation of grain boundary cracking was observed. Similar to the strain-age cracking phenomenon in Ni-base alloys, the rapid formation of κ-carbides at grain boundaries leads to the weakening of grain boundaries.

Abstract Ultrasound can improve the quality of finished products by reducing porosity and enhancing microstructure in selective laser melting, directed energy deposition, and laser beam welding. This study evaluates the efficiency of ultrasound produced by a pulsed laser via the optoacoustic effect. A quantitative model of collapse of vapor-gas bubbles has been developed under the conditions of ultrasonic treatment at near resonance frequencies. Based on the simulation results, the phenomenological expressions are suggested to determine the optimal operating frequency and power for the pulsed laser to alter the microstructure and porosity effectively via cavitation. The analysis is performed for the 316 L stainless steel and titanium Ti-6Al-4 V alloy, which are common in additive manufacturing.

ABSTRACTAdditive Manufacturing (AM) processes have the capability to promote a more rational use of resources, reduce generated waste, as well as repair damaged components to extend their lifespan. With the aim of contributing to a more efficient and environmentally friendlier manufacturing process, this research work investigates the energy consumption of the Laser Directed Energy Deposition process (DED-LB) through the introduction of new parameters in its characterisation, which are energy and mass efficiency. First, the energy efficiency of the laser generator itself has been characterised. Based on the results obtained, it was possible to conclude that pulsed laser operation strategies allow a reduction in consumption of up to 9% compared to a continuous laser operation mode. Second, a design of experiments was carried out to relate the energy and mass efficiency of the deposition process to the input parameters: laser power, feed rate, and mass rate. The parameter that most influences the energy efficiency of the process is the mass rate, where high rates are advisable to maximise its value. On the other hand, it is concluded that an increase in the mass rate or a reduction in the feed rate results in an increase in the mass efficiency

Abstract Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios.

We examined single- and multilayer formations in the directed energy deposition for manufacturing pure copper parts using a blue laser with a wavelength of 445 nm. We investigated the influence of laser power and hatching pitch on the surface quality of single-layer structures as well as evaluated the porosity and dilution of multilayer structures fabricated at various laser powers and powder feed rates using energy-dispersive X-ray spectrometry. In addition, the applicability of the simplified method based on the ratio of the built height and penetration depth to the AM process has been examined, and the predicted elemental content was compared with the results obtained from the SEM–EDS analysis. Based on these findings, a range of building conditions that reduce the dilution, suppress the porosity, and improve the building efficiency of the built parts was established. We found that a good surface quality of the single-layer structure was obtained at laser powers and hatching pitches ranging between 150 and 180 W and 0.4 and 0.5 mm, respectively. A higher laser power and a lower powder feed rate decreased the porosity and increased the building efficiency while promoting dilution with the substrate. At a laser power of 180 W and a powder feed rate of 10 mg/s, the built structure exhibited a minimum porosity of 0.1% and a maximum building efficiency of 36%. Dilution with the substrate was the lowest at a laser power of 180 W and a powder feed rate of 20 mg/s, and the proportion of Cu reached 99.0 wt% at a distance of 200 µm from the built structure–substrate interface. The predict method of the dilution based on the ratio between the built height and penetration depth can be integrated in the AM process despite a low prediction accuracy near the substrate due to the complex mixture in the Fe–Cu system. [ABSTRACT FROM AUTHOR]

Maintaining the stability of laser melt-pool conditions is critical to ensure consistency in cooling and solidification rates during directed energy deposition (DED) process. This serves to minimize degradation in the built accuracy and can improve overall microstructure homogeneity in DED parts. This work presents a novel laser power planning approach by utilizing a deep neural network (DNN) agent to learn the laser power control policy directly from the melt-pool information predicted by the finite element (FE) model. The agent was trained via the deep Q-learning algorithm using a previously developed FE model as the process simulator. The numerical experiment demonstrates that the trained agent is capable of modulating the laser power during the simulated deposition of a new component and can maintain the fluctuation in melt-pool volume within the defined target range. This work shows the potential of using the numerical modeling and reinforcement learning to generate the required laser power planning and modulation in DED process, without requiring physical sensors and closed-loop in-process monitoring and control. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is an additive manufacturing technique that is very well adapted for remanufacturing metallic components. The process works by depositing material onto either a substrate or, in the case of remanufacturing, existing component using a metal wire or powder that is fed into an energy source, usually either a laser or electron beam. DED naturally introduces a large amount of heat into the component in a manner that generally produces both large and sharp temperature gradients. These gradients generate residual stresses in the material which cause the component to warp in ways that are often difficult to accurately predict. To use this method for remanufacturing components with specific geometrical demands, such as turbine blades, any potential warping must be accounted for and minimized. A simulation methodology based on inherent strain (IS) is proposed as a high-throughput high accuracy method of evaluating the warping based on the geometry and parameters of an individual component. The study compares the results of this high throughput IS-based simulation tool with more traditional thermomechanical simulations with respect to both accuracy and time efficiency when applied to an industrial case. [ABSTRACT FROM AUTHOR]

This current study investigates the effect of Direct Energy Deposition (DED) process conditions on the properties and microstructure of M300 maraging steel samples. The investigation centers on two key factors: laser power and deposition environment. The microstructure of this tool steel is analyzed by computing the Primary Cellular Arm Spacing. The findings revealed a significant influence of both inert atmosphere and laser power on cooling conditions. These different cooling rates influence the phase content as demonstrated by X-Ray Diffraction and Electron Backscatter Diffraction measurements. It was demonstrated the presence of different content of residual austenite at cell boundaries. These distinct microstructural features caused variations in the hardness values of the printed samples. Furthermore, a direct aging heat treatment was implemented, that was chosen from Differential Scanning Calorimetry measurements results. This heat treatment proves effective in achieving consistent hardness increases and eliminated the differences among samples built in different process conditions. This outcome suggests the possibility of selecting the most economically viable DED parameters for optimal results. IMPACT STATEMENT: This study innovatively explores how DED process conditions impact M300 maraging steel microstructure, revealing crucial insights for optimizing properties and achieving consistent results, ensuring economically viable applications. [ABSTRACT FROM AUTHOR]

Laser Directed Energy Deposition (LDED) is one of the key technologies in metal additive manufacturing, characterized by long-duration operations, continuous thermal cycling, and a layer-by-layer or track-by-track accumulation process. Under these conditions, ensuring long-term stability during the manufacturing process is a critical factor in overcoming the current application bottlenecks of this technology. Process monitoring and control are essential approaches to improving process stability in the manufacturing field. By detecting changes in the processing state during LDED in real time and preventing defect formation, the reliability of manufacturing quality can be significantly improved, and the risk of scrapping entire components due to localized defects can be avoided. Focusing on monitoring and control in LDED, this paper first reviews the current development of monitoring and control technologies in industrial-grade LDED equipment. Secondly, it analyzes the types of sensing signals available for LDED and their effectiveness in quality monitoring. Then, it elaborates on current control strategies and capabilities based on characteristics such as melt pool morphology, deposited layer geometry, and thermal features. Finally, the paper examines the challenges faced by monitoring and control technologies, proposing potential solutions aimed at advancing LDED technology to meet the manufacturing demands of high-end applications. [ABSTRACT FROM AUTHOR]

Directed Energy Deposition Laser Beam (DED-LB) is a promising additive manufacturing technique that uses a laser source and a powder stream to build or repair metal components. Repair applications offer significant economic and environmental benefits but are more challenging to develop, especially for components that are difficult to process due to their intricate geometries and materials. Process conditions can change precipitously, and it is essential to implement monitoring systems that ensure high process stability and, consequently, superior end-product quality. In the present work, a mid-wave infrared coaxial camera was used to monitor the melt pool geometry. To simulate the challenging repair process conditions of the DED-LB process, experimental tests were carried out on substrates with different thicknesses. The stability of the deposition process on nickel-based superalloys was analyzed by means of MATLAB algorithms. Thus, the effect of open-loop and closed-loop monitoring with back control on laser power on the process conditions was assessed and quantified. Metallographic analysis of the produced samples was carried out to validate the analyses performed by the monitoring system. The occurrence of production defects (lack of fusion and porosity) related to parameters not directly controllable by monitoring systems, such as penetration depth and dilution, was determined. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) processes have the capability to promote a more rational use of resources, reduce generated waste, as well as repair damaged components to extend their lifespan. With the aim of contributing to a more efficient and environmentally friendlier manufacturing process, this research work investigates the energy consumption of the Laser Directed Energy Deposition process (DED-LB) through the introduction of new parameters in its characterisation, which are energy and mass efficiency. First, the energy efficiency of the laser generator itself has been characterised. Based on the results obtained, it was possible to conclude that pulsed laser operation strategies allow a reduction in consumption of up to 9% compared to a continuous laser operation mode. Second, a design of experiments was carried out to relate the energy and mass efficiency of the deposition process to the input parameters: laser power, feed rate, and mass rate. The parameter that most influences the energy efficiency of the process is the mass rate, where high rates are advisable to maximise its value. On the other hand, it is concluded that an increase in the mass rate or a reduction in the feed rate results in an increase in the mass efficiency [ABSTRACT FROM AUTHOR]

The high-entropy alloy (HEA) has recently attracted significant interest due to its novel alloy design concept and exceptional mechanical properties, which may exhibit either a single or multi-phase structure. Specifically, refractory high-entropy alloys (RHEA) composed of titanium, niobium, and nickel-based HEA demonstrate remarkable mechanical properties at elevated temperatures. Additive manufacturing (AM), specifically Direct Energy Deposition (DED), is efficient in fabricating high-entropy alloys (HEA) owing to its fast-cooling rates, which promote uniform microstructures and reduce defects. This study involved the fabrication of the Ti33Nb28Cr11V11Ni17 (Ti-Nb-Cr-V-Ni) RHEA utilizing DED. Additionally, the post-processing of the fabricated alloy is conducted using conventional machining (CM) and laser-assisted machining (LAM). The results indicate thermal conductivity and specific heat increased, whereas tensile strength reduced with rising temperature. Significant softening was observed above 800 °C, resulting in a considerable decrease in tensile strength. Furthermore, the LAM caused material softening and reduced the cutting force by 60.0% relative to CM. Furthermore, the chemical composition of Ti-Nb-Cr-V-Ni remained unaffected even after post-processing with CM and LAM. The research indicates that post-processing with LAM is essential for developing resilient RHEA for practical use. [ABSTRACT FROM AUTHOR]

Arc‐directed energy deposition (DED‐Arc) is a technology for the production of complex and large‐scaled components. It benefits from high deposition rates and low investment costs. However, to further expand the applications of additively manufactured parts, the possibility for implementation in conventionally manufactured assemblies is crucial. Therefore, this study focusses on the structural integrity of welded joints of DED‐Arc and as‐rolled duplex stainless steels and the welded and nonwelded as‐rolled benchmark. While the fraction of the constituents is almost balanced in the as‐rolled condition, the amount of austenite in the DED‐Arc specimens exceeds 60%. The similar weldment is predominantly ferritic and the hybrid welded joint contains ≈30% austenite, which is discussed based on the nickel concentration. Results from microstructure characterization are correlated to the superior yield and ultimate tensile strength of the as‐rolled condition. Under total strain‐controlled fatigue experiments, the hybrid welded joints exhibit enhanced fatigue lives at various strain amplitudes while the base material shows lowest fatigue resistance. Computed tomography and fractography conclude that the structural integrity is not governed by inherent defects, but instead the phase ratio and distribution yield significant differences in the cyclic deformation response as well as an asymmetry of the hysteretic stress–strain behavior. [ABSTRACT FROM AUTHOR]

In this paper, we present the characteristics of our Directed Energy Deposition 3D printing machine LAMDA, focusing on the local shield nozzle and monitoring feedback. We explain the effects of the local shield nozzle, which ensures a wide shielding area, and the monitoring feedback, which stabilizes the melt pool and improves the stability of the metal material and shape, with examples of the resulting structures. [ABSTRACT FROM AUTHOR]