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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.

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]

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]

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]

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]

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]

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]

The finite element analysis (FEA) simulation of directed energy deposition (DED) processes offers many potential cost savings during the build job optimization process, through, e.g., distortion predictions. However, the biggest challenge is the long calculation time, frequently exceeding the actual build time. One way of simplifying the simulation with the aim of reducing the calculation times is the inherent strain method. While this method is already used commercially in the simulation of powder bed-based processes and conventional welding technologies, its use in DED is still the subject of research. In this work, an inverse determination of an inherent strain is carried out on a 20-layer-high, single-track-wide wall, common theories are reviewed, and an approach based on thermal strain is introduced. As a result, the calculation time could be reduced by 83% and the accuracy remained at 92%. [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]

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]

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. [ABSTRACT FROM AUTHOR]

The high-temperature durability performance plays a crucial role in the applications of single-crystal (SX) superalloys repaired by laser-directed energy deposition (L-DED). A specialized heat treatment process for L-DED-repaired SX superalloys was developed in this study. The effect of the newly customized heat treatment on the microstructure and high-temperature mechanical properties of DD32 SX superalloy repaired by L-DED was investigated. Results indicate that the repaired area of the newly customized heat treatment specimen still maintained a SX structure, the average size of the γ′ phase was 236 nm, and the volume fraction was 69%. Obviously recrystallized grains were formed in the repair area of the standard heat treatment specimens, and carbide precipitated along the grain boundary. The size of the γ′ phase was about 535 nm. The high-temperature durable life of the newly custom heat treatment specimen was about 19.09 h at 1000 °C/280 MPa, the fracture mode was microporous aggregation fracture, and the fracture location was in the repair area. The durable life of the standard heat treatment specimen was about 8.70 h, the fracture mode was cleavage fracture, and the fracture location was in the matrix area. The crack source of both specimens was interdendrite carbide. [ABSTRACT FROM AUTHOR]