Additive Manufacturing Techniques Research Articles

Assessment of Additive Manufactured Micro-Channel Characteristics: Impact of Hydraulic Diameter Evaluation

Abstract Among internal cooling techniques for gas turbines components, skin cooling is recognized as one of the most promising, especially thanks to the spread of additive manufacturing techniques. In this regard, several studies have tried to characterize heat transfer and pressure loss performance of additive manufactured micro-channels, as well as the impact of AM characteristic parameters, mainly in the form of building angle, and resulting surface roughness and channel shape. The open literature offers several correlations in terms of friction and heat transfer coefficient using a representative hydraulic diameter. Despite that, little attention is given on the importance of such characteristic length definition which may lead to under/over estimation of the cooling rates when correlations are used in the design In this work, coupons featuring circular micro-channelswith diameters of 0.5 and 1 mm and different building angles have been tested, to retrieve pressure losses and an average heat transfer coefficient through a lumped approach. The coupons were additive manufactured using the Laser Powder Bed Fusion (L-PBF) technique. Exploiting the experimental survey, the impact of the considered hydraulic diameter was investigated, by comparing different approaches based on a direct measurement. An additional and new approach, aimed at the identification of an “effective” diameter, based on fluid-dynamic considerations, was also considered. The data correlation and the comparison with available correlations from different authors showed the newly proposed approach to provide superior scaling capabilities and, in turn, allowing for the development of more accurate prediction methods

An iterative price-based combinatorial double auction for additive manufacturing markets

The increasing adoption of additive manufacturing (AM) in the industrial sector is leading to an imbalance between supply and demand of additively manufactured subcomponents: companies demanding AM services require very specific products and AM suppliers differ widely in their capabilities. Some existing proposals aim to help match supply and demand by merely making customer–supplier allocations. Only a few recent works go beyond allocation issues and propose market mechanisms to also address pricing aspects. However, we observe that these mechanisms do not fully exploit the potential of additive manufacturing techniques. The aim of this paper is to design a market mechanism that considers the particularity of AM techniques, wherein suppliers can benefit from manufacturing multiple heterogeneous parts from multiple customers in the same build area to increase production throughput. This market mechanism has been implemented as an iterative combinatorial double auction that adapts to this feature of the AM market: customers will bid to get their orders produced and suppliers will submit asking quotes to win the production of combinations of those orders. The mechanism solves the allocation and pricing of AM orders while seeking the maximization of social welfare. The procedure is simulated in a theoretical environment to evaluate its performance and to identify the most appropriate conditions for its implementation in a real environment. Unlike other existing proposals for client-supplier allocation mechanisms in additive manufacturing, the proposed mechanism allows a single supplier to produce a combination of orders from different clients, leading to a pricing system that maximizes social welfare without participants disclosing sensitive information.

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

Effect of printing temperature on the mechanical properties of 3D printed polyphenylene sulfide (PPS) during simple and repeated impact tests

AbstractFused filament manufacturing (FFF), also known as 3D printing, is one of the most commonly used additive manufacturing techniques for creating high-quality materials. This process demonstrates the intricacies and challenges involved in choosing appropriate manufacturing parameters to achieve the desired outcomes. Among these critical parameters is the nozzle temperature, which can be adjusted to enhance the mechanical properties of the 3D-printed Polyphenylene Sulfide (PPS) parts. The main objective of this study is to investigate the influence of the printing temperature on the mechanical properties and failure characteristics of 3D printed polyphenylene sulfide (PPS) parts during impact testing. To do this, a series of simple and repeated impact tests were carried out on printed PPS samples in the nozzle temperature range (320–350 °C). CHARPY tests were carried out on the samples manufactured with different sequences for the optimal orientation of the filaments. Furthermore, the impact energy absorption capacity and the induced damage as a function of nozzle temperature were evaluated. CHARPY test results showed that samples with stacking sequence (0/0) had the best impact resistance and specific absorbed energy (SEA). This sequence, printed horizontally, was used to test different print temperatures in single and repeated impact tests. Furthermore, the results indicated that samples printed with a nozzle temperature of 340 °C exhibited higher CHARPY impact resistance and specific absorbed energy (SEA), with a percentage difference of 45.57%, 41.95% and 44.21% compared to samples printed with nozzle temperatures of 320 °C, 330 °C and 350 °C respectively. For repeated impact tests, the results also show that samples printed with a nozzle temperature of 340 °C have a higher initial energy absorption rate and a greater number of impacts before complete failure of the sample. This result proves also that the changing of nozzle temperature does not have a significant effect on the induced damage after CHARPY and repeated impact.

Study of mechanical and metallurgical properties of wire arc additively manufactured inconel 718 alloy

The current research work deals with the investigation of mechanical, wetting transition on grain boundary (GB) and metallurgical properties of Inconel 718 alloy used in aerospace industrial applications. The fabrication of thin-wall structure (multilayer structure) is fabricated using wire arc additive manufacturing (WAAM) technique. The novelty of this method is that gas metal arc welding (GMAW) is used as energy source and cold metal transfer (CMT) is used as mode of droplet transfer. Four tensile samples are extracted asper ASTM E8/E8M standards and three microstructure samples are extracted from thin wall structure. Optical microscopy (OM), SEM, EDAX, and EBSD analysis were carried out. In addition, Hardness and tensile tests, as well as fractography analysis were conducted. The grain boundary wetting transition (GBWT) analysis is also carried out. SEM analysis reveals that Nb-rich MC-type carbide contains grain boundaries of droplets, indicating incomplete wetting. Laves and delta (δ) phases are also identified as liquids of a continuous layer separated by Ni elements, indicating complete wetting. EBSD analysis indicates the average grain size as 251.2 μm. The average value of micro hardness as 417.3 HV and UTS as 609.75 MPa are achieved.

Melt Pool Changes Characterization in Laser-Processed H11 Hot Work Tool Steel Using Point-by-Point Scanning Mode towards LPBF Process Optimization.

Additive manufacturing techniques employing laser-based metal melting have garnered significant attention within the scientific community. Despite a decade of comprehensive research on the fundamentals of these techniques, there still remain unexplored facets related to heat flux impact on metallic alloys' properties. Particularly, the effects of point-by-point laser operation on melt pool formation in metallic materials still remain unclear. Thus, this study focuses on the implications of laser metal melting, particularly investigating a point-by-point laser mode operation's influence on melt pool formation and its geometry in the phase-transformation-sensitive material H11 hot work tool steel. To examine the melt pool, singular laser tracks with various laser parameters were scanned across H11 sheet metal, which allowed for the elimination of layer-by-layer heat cycles' influence on the melt pool's microstructure. Samples were examined by means of metallography, revealing significant differences in the melt pool's depth, influenced mostly by exposure time rather than volumetric energy density. Heat-affected zone effects were found to have a limited range and thus potentially marginal effects in layer-by-layer manufacturing conditions. At the same time, retained austenite concentrations near fusion lines have been found within melt pools, suggesting potential micro-segregation of the alloying additions. The results present guidelines towards laser melting processes optimization.

Enhanced Feedstock Processability for the Indirect Additive Manufacturing of Metals by Material Extrusion through Ethylene-Propylene Copolymer Modification.

Filament-based material extrusion (MEX) represents one of the most commonly used additive manufacturing techniques for polymer materials. In a special variation of this process, highly filled polymer filaments are used to create metal parts via a multi-step process. The challenges associated with creating a dense final part are versatile due to the different and partly contrary requirements of the individual processing steps. Especially for processing in MEX, the compound must show sufficiently low viscosity, which is often achieved by the addition of wax. However, wax addition also leads to a significant reduction in ductility. This can cause filaments to break, which leads to failure of the MEX process. Therefore, the present study investigates the influence of different ethylene-propylene copolymers (EPCs) with varying ethylene contents as a ductility-enhancing component within the feedstock to improve filament processing behavior. The resulting feedstock materials are evaluated regarding their mechanical, thermal and debinding behavior. In addition, the processability in MEX is assessed. This study shows that a rising ethylene content within the EPC leads to a higher ductility and an enhanced filament flexibility while also influencing the crystallization behavior of the feedstock. For the MEX process, an ethylene fraction of 12% within the EPC was found to be the optimum regarding processability for the highly filled filaments in MEX and the additional processing steps to create sintered metal parts.

3D printed microcrystalline CsI:Tl composite scintillating thin films for X-ray imaging

The utilization of additive manufacturing techniques, especially Digital Light Printing (DLP), in fabricating CsI:Tl scintillator films demonstrates considerable potential for streamlining the production of scintillators tailored for X-ray imaging applications. This research focuses on the fabrication of CsI:Tl-based composite plastic scintillator thin films. In this study, circular films measuring 1-inch in diameter and 0.1 mm & 0.2 mm in thickness are being produced and tested for gamma photon counts under alpha and gamma radiation. To establish the stopping power range of the films, a Monte Carlo based GEANT4 simulation has been carried out. Additionally, investigations into their suitability for X-ray imaging applications are being conducted, revealing the spatial resolution of the films (0.2 and 0.1 mm) between 100 and 130 μm and 1.26 lp/mm with a contrast range of 4.0–12.3 %. The observed decrease in spatial resolution and contrast for the 0.2 mm thick film is attributed to the thickness increase exacerbating the scattering phenomenon while simultaneously enhancing the X-ray stopping power. This highlights the significance of inherent trade-off between maximizing spatial resolution and compromising light yield of 0.1 mm films compared to the 0.2 mm thick film. By utilizing 3D printing, this approach offers a cost-effective and time-efficient method for producing thin-film scintillators with enhanced flexibility and customization options compared to conventional methods.

Impact response of additively manufactured density-graded open-cell foams

Additive manufacturing has made it possible to fabricate materials that were unachievable with traditional methods. This study focuses on understanding the deformation behavior and energy absorption mechanics of additively manufactured cellular materials with gradually varying densities. Foams have unique deformation behavior due to their intricate topology and composition, resulting in excellent energy dissipation capability. Varying the density can significantly influence their deformation response and improve energy absorption and impact resistance. Voronoi tessellation is employed to model the foams, as it effectively captures the cell morphology in foam structures and produces stochastic cellular topologies accurately. Resin-based additive manufacturing techniques are employed to fabricate cellular materials with varying density configurations for low-velocity and high-velocity impact experiments. The study demonstrates that density-graded foams effectively dissipate a broad spectrum of impact energies, surpassing uniform counterparts by transmitting reduced stress, especially at lower energy levels. This characteristic enhances their suitability for advanced energy absorption applications. The results also show that at high impact velocities, the direction of density gradation influences energy dissipation and peak stress transmission.

Industrial potential of additive manufacturing of transparent ceramics: A review

Transparent ceramics is a unique class of materials, with performance comparable to single crystals, but a high process flexibility given by ceramic technology. Currently, traditional ceramic shaping technologies reliably produce components but are limited in terms of shapes and the use of multiple compositions in a single component. The presented review aims to illustrate how the introduction of additive manufacturing (AM) technology in the production of transparent ceramic components opens new possibilities, both thanks to the high variability of shapes and thanks to the high precision in producing parts with a controlled variation of composition. Within this review, several AM techniques and their current state of the art are analysed, with focus on their advantages in producing transparent ceramics, along with the associated challenges and limitations. The future perspective and possibilities for an industrial production are discussed, with emphasis on the most promising AM techniques, direct ink writing and vat photopolymerisation, pointing out the future scenario for the transparent ceramics market.

A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

Abstract This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors, such as process parameters, defects, cooling rates, thermal histories, and machine stability, have a prominent impact on AM products' properties and microstructure. To establish the relationship between these AM factors and the end product properties and microstructure is difficult. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This paper discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM fabricated material's microstructures and acceleration of microstructure prediction procedures are discussed in this paper.

Utilizing Additive Manufacturing for Economical Prosthetic Limb Prototyping: A Guide from Regression Modelling

This study investigated the development of a low-cost prosthetic limb using additive manufacturing techniques, guided by anthropometric data and regression modeling. Anthropometric measurements were collected from 250 healthy young adults to characterize limb dimensions. Statistical analysis revealed the mean, standard deviation, and range for right and left leg and elbow lengths. A regression model was formulated to estimate limb lengths based on these measurements. A robust regression model demonstrated a significant correlation (r = 0.993, r² = 0.986) between right limb measurements and corresponding left limb measurements, indicating that right leg and elbow lengths could accurately predict left side measurements. The derived regression equation (RLL = 2.253 + 0.959 LLL + 0.054 LEL) provides a practical tool for designing customized prosthetics. Utilizing 3D printing technology, these findings can enhance the production of tailored prosthetic limbs, improving comfort and functionality for users. The study recommends further validation across diverse populations, integration into clinical practices, and continued research and development supported by policy and funding initiatives. These steps will advance the accessibility and effectiveness of personalized prosthetic solutions.

Space–time topology optimization for anisotropic materials in wire and arc additive manufacturing

Wire and Arc Additive Manufacturing (WAAM) has great potential for efficiently producing large metallic components. However, like other additive manufacturing techniques, materials processed by WAAM exhibit anisotropic properties. Assuming isotropic material properties in design optimization thus leads to less efficient material utilization. Instead of viewing WAAM-induced material anisotropy as a limitation, we consider it an opportunity to improve structural performance. This requires the integration of process planning into structural design. In this paper, we propose a novel method to utilize material anisotropy to enhance the performance of structures both during fabrication and in their use. Our approach is based on space–time topology optimization, which simultaneously optimizes the structural layout and the fabrication sequence. To model material anisotropy in space–time topology optimization, we derive the material deposition direction from the gradient of the pseudo-time field, which encodes the fabrication sequence. Numerical results demonstrate that leveraging material anisotropy effectively improves the performance of intermediate structures during fabrication as well as the overall structure.

A comparative study on nanoscale mechanical properties of CrMnFeCoNi high-entropy alloys fabricated by casting and additive manufacturing

Additive manufacturing (AM) has emerged as a pioneering method for fabricating high entropy alloys (HEAs), yet a comprehensive comparison of their nanoscale mechanical properties with those produced by the conventional casting method remains unexplored. In this study, the nanoindentation was utilized to investigate the nanoscale elastic and plastic characteristics in both additive-manufactured (AM-ed) and as-casted single-phase face-centered cubic (FCC) equiatomic CrMnFeCoNi HEAs. Herein, the hardness, reduced modulus, indentation size effect (ISE), yield strength, fracture toughness, and strain rate sensitivity were comprehensively investigated. The results indicated that the hardness of AM-ed HEA was higher than the as-casted HEA, and the reduced modulus values showed no notable distinction between the two samples. The AM-ed HEA demonstrated simultaneous enhancements in yield strength and fracture toughness compared to the as-casted HEA. The as-casted HEA possessed a more distinct indentation size effect (ISE) than the AM-ed HEA. It was observed that the AM-ed HEA exhibited relatively lower strain rate sensitivity and a larger activation volume. This direct comparison of the mechanical properties and deformation mechanisms from a nanoscale view offers unique insights for optimizing and advancing AM techniques in the fabrication of HEAs.

Development of a Novel Transonic Fan Casing Making Use of Rapid Prototyping and Additive Manufacturing

Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. Compared to traditional machining, AM techniques are simpler in execution for design engineers because they do not require detailed engineering drawings and they typically make use of the nominal geometry in computer models. A novel transonic fan casing assembly has been developed that makes use of AM inserts surrounding the rotor to provide an opportunity to cost-effectively change the corresponding flowpath. The rapid prototyping design philosophy developed from this work will allow for numerous experimental studies into the effects that different design parameters of casing geometries have on fan aerodynamic performance. A fan stage representative of a small turbofan engine was successfully tested with smooth-walled, additively manufactured inserts as a baseline case for future configurations. Before installing the 3D printed casing assembly, computational thermal stress analysis was performed to reduce the risk in implementation due to the demanding environment associated with the rotor. AM components and materials typically have nonlinear mechanical properties, adding to the complexity of the structural analysis. As part of the research, steady aerodynamic performance was measured over the entire relevant operating range of the fan.

Jet on demand—A pneumatically driven molten metal jetting method for printing crack-free aluminum components

Additive manufacturing (AM) of many traditional aluminum alloys is difficult due to hot cracking during cooling, which motivates investigating alternative AM methods that can mitigate this challenge. Here we demonstrate a new pneumatically driven molten metal jetting (MMJ) AM technique which uses a longer pressure pulse width to produce a jet of liquid metal that reaches the heated build plate. The “jet on demand” technique is utilized to build Al-6061 parts on heated build plates. Due to the large thermal mass contained in each jet, excellent adhesion is observed between droplets and layers while still maintaining dimensional control to produce parts with high relative densities (>98%). While as-printed parts exhibit different microstructure and hardness than traditional Al-6061, both microstructure and hardness are restored to traditionally processed values through a traditional T6 heat treatment. Microhardness values of 104 HV were obtained for printed Al-6061, which compares well to wrought properties. We observe that high build plate temperatures allow for lower solidification rates and eliminate hot cracking. These results point to a method for additively manufacturing traditional aluminum or other alloys that cannot currently be additively manufactured due to hot cracking.

Microstructure evolution and mechanical property of B4C/Ti65 composites via laser directed energy deposition

Application of laser additive manufacture technique to fabricate the discontinuous reinforced high-temperature titanium matrix composites found its great potential in the complex components of aerospace industry. This work prepared the B4C/Ti65 composites by laser directed energy deposition (LDED) with special attention paid to the relationship between microstructure and mechanical property of composites. The microstructure of B4C/Ti65 composites under different laser power and B4C content were investigated, based on which the tensile strength and ductility evolution and the underlying strengthening mechanism of the B4C/Ti65 composites were discussed. The submicron TiBw were randomly distributed in the composites with additions of 0.1 wt% and 0.2 wt% B4C. The TiBw and TiCp in the composites with addition of 0.3 wt% B4C exhibited a uniform distribution rather than distributed along the prior β grain boundaries at high laser power. There existed the network structure of TiBw and TiCp in the 0.5 wt% B4C/Ti65 composites. TiCp always adhered to TiBw, and both TiBw and TiCp acted as the nucleation sites for equiaxed α grain, which is responsible for the finer α colonies that benefit to strength with higher B4C content. The tensile tests showed that the 0.3 wt% B4C/Ti65 composite shows a good combination of ultimate tensile strength (1201 MPa) and elongation (5.54 %). The grain refinement strengthening played the dominant role in the strength increment of the B4C/Ti65 composites. In addition, the formation of clustered submicron-TiBw and network structure of TiBw and TiCp in the 0.5 wt% B4C/Ti65 composites greatly upgraded the yield strength.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Interface characteristics and performance of additively manufactured steel-titanium bimetals prepared through composition gradient layers

In this study, laser-directed energy deposition (LDED), a powder-feeding laser additive manufacturing technique, was utilized to fabricate bimetals comprising high-strength steel (HSS) and Ti6Al4V (TC4). Through meticulous compositional control, four distinct regions (labeled Areas 1 through 4) comprising Fe-Ti intermetallics with varying sizes and morphologies were obtained at the LDEDed HSS/TC4 interface. These zones exhibited thicknesses of approximately 190 μm, 210 μm, 525 μm, and 205 μm, respectively. The effect of these intermetallics on the performance of LDEDed HSS/TC4 bimetals was investigated. The results indicated that most Fe-Ti intermetallics, characterized by reticular/granular morphologies or fine precipitates, were dispersed at the bonded interfaces (Areas 1, 3, 4). Nevertheless, irregular and long-striped C14_Laves phases were observed in Area 2, indicating the presence of a pure Fe2Ti region. Performance testing revealed that fractures predominantly occurred within a hard and brittle Fe2Ti region (Hardness: 891.5HV) during mechanical grinding or tensile clamping, highlighting the formidable challenge of achieving high-quality LDEDed HSS/TC4 bimetals through a component-regulated building strategy. The difficulty arises from the high Fe content in HSS and high Ti content in TC4, resulting in the formation of a pure Fe2Ti region at the interface. Thermodynamic calculations suggest that the composition-gradient-layer strategy may not be optimal for the integrated laser additive manufacturing of HSS and TC4. These findings provide valuable insights into the design of steel-titanium functional gradient materials and other multi-material components.

An Overview on Additive Manufacturing of Duplex Stainless Steels: Microstructure, Mechanical Properties, Corrosion Resistance, Postheat Treatment, and Future Perspectives

Additive manufacturing (AM) is a cutting‐edge technique for constructing intricate components with unique microstructural features and strength comparable to wrought alloys. Due to their exceptional corrosion resistance and mechanical properties, duplex stainless steels (DSS) are used in a wide range of critical applications. Over the past several years, a substantial body of research has been conducted on the AM of DSS. In‐depth knowledge is required to understand the complete benefits of the AM process. This review overviews the AM‐processed DSS parts based on process‐specific microstructural changes, mechanical behavior, electrochemical performance, and postheat treatment processes based on the classifications of directed energy deposition and powder bed fusion AM techniques along with future perspectives. Major challenges in AM of DSS are optimizing the austenite–ferrite fractions and controlling the formations of deleterious phases. This review will be extensively useful to researchers and industries working in the AM of DSS.