Metal Additive Manufacturing Processes Research Articles

The Production of Three-Dimensional Metal Objects Using Oscillatory-Strain-Assisted Fine Wire Shaping and Joining

Material shaping and joining are the two fundamental processes that lie at the core of many forms of metal manufacturing techniques, including additive manufacturing. Current metal additive manufacturing processes such as laser/e-beam powder bed fusion and Directed Energy Deposition predominantly use heat and subsequent melt–fusion and solidification to achieve shaping and joining. The energy efficiency of these processes is severely limited due to energy conversion losses before energy is delivered at the point of melt–fusion for shaping and joining, and due to losses through heat transfer to the surrounding environment. This manuscript demonstrates that by using the physical phenomenon of lowered yield stress of metals and enhanced diffusion in the presence of low amplitude high frequency oscillatory strain, metal shaping and joining can be performed in an energy-efficient way. The two performed simultaneously enable a metal additive manufacturing process, namely Resonance-Assisted Deposition (RAD), that has several unique capabilities, like the ability to print net-shape components from hard-to-weld alloys like Al6061 and the ability to print components with a very high aspect ratio. In this study, we show this process’s capabilities by printing solid components using aluminum-based metal alloys.

Size matters: Exploring part size effects on microstructure, defects, and mechanical property in optimized laser powder bed fusion (L-PBF) additive manufacturing

In laser-based metal additive manufacturing (MAM), effective process optimization requires a thorough analysis of variations across the process window. Despite the common practice of utilizing small-sized coupons to optimize process parameters in laser-based MAM, this study challenges this convention. Through an analysis of microstructure and defect differences in Fe-Ni material parts produced via laser-powder bed fusion (L-PBF), the study reveals significant discrepancies in microstructure, defect and mechanical property based on part size. As part size increases, lack-of-fusion defects and cracks become more pronounced, resulting in a decrease in density from 8.11 g/cm3 to 8.02 g/cm3, leading to constrained grain growth with a decrease of grain size from 41.88 μm to 23.07 μm in the scanning plane. Each defect and microstructural difference was traced back to variations in thermal history based on part size. Finite element method simulations, highlighting differences in thermal history from scanning a single layer to the entire part, showed that an increase in scan length with a widened scan area enlarged the period of the thermal history cycle, leading to considerable cooling. This study underscores crucial considerations for future researchers engaged in process optimization for novel alloy systems using laser-based MAM processes.

Phenomenological sintering model and experimental validation of gravity-induced distortions in binder-jetted stainless steel components

Sintering is an important consolidation step employed in sinter-based metal additive manufacturing processes. Binder Jetting (BJT) starts with green components with low green density (40-60%) that results in large sintering shrinkages and geometrical shape distortions caused by external forces (e.g. gravity). Consequently, the prediction of the final sintered geometry is crucial during the design process. In this work, a novel sintering simulation framework for gravity-affected sintering of stainless-steel components is presented, including the Rios-Olevsky-Hryha sintering model and the methodology for the identification of the required material parameters. The constitutive law includes material constants to account for the powder packing effects and the delta-ferrite transformation occurring at high temperatures. The material shear viscosity was explicitly related to the equilibrium phase fraction of austenite and delta-ferrite during sintering temperatures. Dilatometry experiments were conducted and followed by the data postprocessing for the model calibration. The calibrated model was incorporated in a FEM code and validated against experimental data from BJT sintered components, showing the remarkably accuracy of the numerical simulations, with small geometric deviations (0.56 mm) related to the assumption of isotropic shrinkage in the model proposed. In parallel, other alternative models were implemented based on different normalized bulk viscosity formulations, which underestimate/overestimate the sintered distortions.

A Study on the Change of Mechanical Properties of Inconel 625 with Interlayer Temperature in a Metal Additive Manufacturing Process Using CMT Welding

A Study on the Change of Mechanical Properties of Inconel 625 with Interlayer Temperature in a Metal Additive Manufacturing Process Using CMT Welding

Evaluation of Powder- and Extrusion-Based Metal Additive Manufacturing Processes for the Sustainable Fabrication of Spare Parts in Electromobility

Evaluation of Powder- and Extrusion-Based Metal Additive Manufacturing Processes for the Sustainable Fabrication of Spare Parts in Electromobility

Powder-size driven facile microstructure control in powder-fusion metal additive manufacturing processes

Microstructure control in metal additive manufacturing is highly desirable for superior and bespoke mechanical performance. Engineering the columnar-to-equiaxed transition during rapid solidification in the additive manufacturing process is crucial for its technological advancement. Here, we report a powder-size driven melt pool engineering approach, demonstrating facile and large-scale control in the grain morphology by triggering a counterintuitive response of powder size to the additively manufactured 316 L stainless steel microstructure. We obtain coarse-grained (>100 μm) or near-monocrystalline microstructure using fine powders and near-equiaxed, fine-grained (<10 μm) microstructure using coarse powders. This approach shows resourceful adaptability to directed energy deposition and powder-bed fusion with no added cost, where the particle-size dependent powder-flow preheating effects and powder-bed thermophysical properties drive the microstructural variations. This work presents a pathway for leveraging feedstock particle size distribution towards more controllable, cost-effective, and sustainable metal additive manufacturing.

Process parameter optimization for laser powder directed energy deposition of Inconel 738LC

Processing nickel-based superalloys is one of the major challenges in the additive manufacturing (AM) field. Nickel-based superalloys consist of several alloying elements, which make them prone to the formation of undesired phases and cracking due to the complex thermodynamics during the metal AM processes. In order to produce defect-free parts from nickel-based superalloys via metal AM processes, optimization of process parameters is required. In this study, the effect of laser powder directed energy deposition (LP-DED) process parameters on the geometrical properties and porosity ratio of Inconel 738 LC (low carbon) is investigated. Geometrical specifications, including the track height, width, and wetting angle are the major factors that indicate the quality of the final product. The analysis of variance (ANOVA) and response surface methodology (RSM) were developed in order to predict the geometrical specifications of single tracks from the key process parameters, namely laser power, scan speed, and powder feed rate. An optimum set of process parameters was obtained through multi-objective optimization by minimizing the porosity ratio, crack formation and maximizing the deposition rate. The results were verified and a good agreement with the experimental measurements was achieved. The produced bulk sample showed less than one percent porosity content, which indicates the effectiveness of single-track optimization prior to bulk sample production. The overall microstructure of the bulk sample showed a high content of MC carbides resulting from the abundance of alloying elements.

Untapped Opportunities in Additive Manufacturing with Metals: From New and Graded Materials to Post-Processing

Metal additive manufacturing (AM) is an innovative manufacturing method with numerous metallurgical benefits, including fine and hierarchical microstructures and enhanced mechanical properties, thanks to the utilization of a local heat source and the rapid solidification nature of the process. High levels of productivity, together with the ability to produce complex geometries and large components, have added to the versatile applicability of metal AM with applications already implemented in various sectors such as medicine, transportation, and aerospace. To further enhance the potential benefits of AM in the context of small- to medium-scale bulk production, metallurgical complexities should be determined and investigated. Hence, this review paper focuses on three significant metallurgical aspects of metal AM processes: in situ alloying, functionally graded materials, and surface treatments for AM parts. The current text is expected to offer insights for future research works on metal AM to expand its potential applications in various advanced manufacturing sectors.

Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition

Abstract Data-driven research in additive manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature emerging. The knowledge in these works consists of AM and artificial intelligence (AI) contexts that haven't been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as transfer learning (TL). We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featured into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between two flagship metal AM processes: laser powder bed fusion (LPBF) and directed energy deposition (DED). The relatively mature LPBF is the source while the less developed DED is the target. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.

AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification

ABSTRACT Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AM-SegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (∼96%) and processing speed (< 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes’ governing physics.

Additive friction stir deposition of super duplex stainless steel: Microstructure and mechanical properties

Additive Friction Stir Deposition (AFSD) is an emerging solid-state metal additive manufacturing (AM) process that offers several key benefits, including high deposition rates and wrought-equivalent mechanical properties even in the as-deposited condition. The work presented is the first study to report on the development of microstructure and mechanical properties of AFSD-processed duplex stainless steel (DSS2507). The banded microstructure of the starting material was remarkably affected by AFSD processing; the austenite grains exhibited a refined and equiaxed morphology, while the ferrite grains appeared slightly larger and elongated. Microstructural observations revealed that the potential mechanism of microstructure evolution in austenite was discontinuous dynamic recrystallization (DDRX), while in ferrite, it was continuous dynamic recrystallization (CDRX). The occurrence of multiple thermal cycles during the AFSD process resulted in σ phase precipitation, which in turn led to considerable variation in mechanical properties with respect to the build direction. The top region of the as-built part with an insignificant σ phase fraction showed improved tensile strength and ductility combination compared to the as-received DSS2507 as well as other AM-processed DSS2507 alloys.

Advances in Fatigue Performance of Metal Materials with Additive Manufacturing Based on Crystal Plasticity: A Comprehensive Review.

Using metal additive manufacturing processes can make up for traditional forging technologies when forming complex-shaped parts. At the same time, metal additive manufacturing has a fast forming speed and excellent manufacturing flexibility, so it is widely used in the aerospace industry and other fields. The fatigue strength of metal additive manufacturing is related to the microstructure of the epitaxially grown columnar grains and crystallographic texture. The crystal plasticity finite element method is widely used in the numerical simulation of the microstructure and macro-mechanical response of materials, which provides a strengthening and toughening treatment and can reveal the inner rules of material deformation. This paper briefly introduces common metal additive manufacturing processes. In terms of additive manufacturing fatigue, crystal plasticity simulations are summarized and discussed with regard to several important influencing factors, such as the microstructure, defects, surface quality, and residual stress.

Effect of heat treatment on microstructure and mechanical properties of AlSi10Mg fabricated using laser powder bed fusion

Thermal heat treatment is an important step in many metal additive manufacturing processes, particularly laser powder bed fusion (PBF-LB), as it can homogenize the microstructure and enhance the properties of the resulting parts. The effects of post-processing via direct ageing (DA), stress-relieving (SR), and DA or SA followed by annealing or hot isostatic pressing (HIP), on the microstructure, porosity, and resultant mechanical properties of AlSi10Mg samples manufactured using PBF-LB were investigated in this study. Samples were fabricated over a range of processing conditions to probe different starting microstructures and pore features. Porosity and sub-grain solidification cells were characterized because these features are known to influence strength, ductility, and microhardness. DA increased strength at the expense of ductility due to the precipitation of Si-rich particles while SR improved ductility at the expense of strength due to the broken Al/Si eutectic microstructure. Subsequent annealing of DA samples resulted in further breakdown of the Al/Si eutectic microstructure as well as coarsening of Si-rich precipitates, resulting in lower strength and higher ductility than DA alone. HIP resulted in near fully dense and homogenized samples with coarse Si particles, significantly increasing elongation over all other heat treatments. This study shows that processing parameters and heat treatments can be used to tailor porosity, cell size, and resultant mechanical properties of PBF-LB AlSi10Mg.

Damage modeling in additive manufacturing processes for metals

Damage modeling in additive manufacturing processes for metals

Assessing metal powder quality for additive manufacturing using diffuse light spectroscopy

The ability to maintain repeatable quality of metal powder is fundamental to a robust metal additive manufacturing (AM) process. The main powder degradation mechanisms in powder-based AM are the shift in the powder size distribution (PSD) and an oxygen pickup. In this study, we purposely oxidized and fractionated titanium-based and nickel-based alloy powders to evaluate the usefulness of diffuse light spectroscopy in detecting powder condition changes. For the experiment, six gas-atomized powders were used: Titanium grade 2, Ti-5Al-5Mo-5V-1Cr-1Fe, Ti-6Al-4V, Inconel 718, Inconel 625 and CM 247-LC. All powders were tested in terms of their morphology, chemical composition, and diffuse light reflectance. A strong linear correlation of the same character was observed between both PSD and reflectance, and between oxidation level and reflectance. With increasing particle sizes and oxygen levels, a decrease of reflectance is observed. We conclude that diffuse light spectroscopy is a promising measurement method for AM metal powders.

Factors affecting particle characterization of powders used in additive manufacturing

The present study analyze the effects of sample preparation and data analysis techniques for different experimental methods of metal powder characterization. The aim is to propose a robust strategy for quantifying powder particle size distribution and morphology of relevance for metal additive manufacturing (MAM) processes, in particular for powders with nearly spherical particle shape. Laser diffraction (LD), scanning electron microscopy (SEM) and high-resolution X-ray computed tomography (XCT) are used to analyze three different stainless-steel 316L powders. The results obtained demonstrate that the powder sample itself and the sample preparation, both have a significant effect on the obtained values. For the imaging processing methods, the post-processing route is an important part of the characterization and strongly affects the results. However, proper sample preparation is shown to ease this post-processing. Given the high sphericity of the powders used, it is shown that 2D and 3D methods generally lead to the same result when the 2D data are properly transformed to 3D space. Guidelines to overcome the shortcomings of the different techniques are suggested.

Object Detection and Text Recognition for Immersive Augmented Reality Training in Laser Powder Bed Fusion

Professional Training for laser powder bed fusion equipment operation typically is time consuming and expensive for end-users in metal Additive Manufacturing (AM). This study proposes a practical solution by leveraging Hyperskill software to develop a immersive training program compatible with Augmented Reality (AR) devices. The program incorporates the YOLOv7 object detection algorithm and the CRAFT(Character Region Awareness for Text detection) Four-stage text recognition algorithm, seamlessly bridging the gap between reality and simulation. Through a human subjects study, we demonstrate the effectiveness and viability of the AR program, showcasing its capacity to deliver real-time, comprehensive training in metal AM processes. This approach offers a balanced and efficient solution for improving professional training in the field of metal AM.

Effect of re-lasing on the fatigue properties of 316L Stainless Steel Produced by laser powder bed fusion

Despite the great success of the metal additive manufacturing process by Laser Fusion on Powder Bed (L-PBF), this technique still lacks maturity in several areas. Among its major challenges, the low fatigue strength of the L-PBF parts is caused, among other features, by two main types of defects. Firstly, the L-PBF process generates a high surface roughness, mainly linked to the phenomenon of partial powder melting on the part surface. Secondly, internal defects are created during the building process and are randomly distributed in the material. The present study investigates the effect of a second lasing on the improvements of the surface roughness, defects population as well as fatigue properties of L-PBF parts. Based on preliminary experiments, two re-lasing strategies were selected. A high cycle fatigue campaign was subsequently carried out with a stress ratio of R= -1 on samples with or without re-lasing, in the as-built state or after polishing. Specimens printed in Net-Shape showed even after re-lasing and polishing, a much lower fatigue strength than those machined in the bulk after additive manufacturing. In addition, results show that the tested re-lasing conditions has a beneficial effect on the quasi-static and fatigue strength of the 316L obtained by L-PBF.

Comparative Life Cycle Assessment of SLS and mFFF Additive Manufacturing Techniques for the Production of a Metal Specimen.

This work is part of a research project aimed at developing a bio-based binder, composed mainly of polylactic acid (PLA), to produce Ti6Al4V feedstock suitable for use in MAM (Metal Additive Manufacturing) via mFFF (metal Fused Filament Fabrication), in order to manufacture a titanium alloy specimen. While in Bragaglia et al. the mechanical characteristics of this sample were analyzed, the aim used of this study is to compare the mentioned mFFF process with one of the most used MAM processes in aerospace applications, known as Selective Laser Sintering (SLS), based on the Life Cycle Assessment (LCA) method. Despite the excellent properties of the products manufactured via SLS, this 3D printing technology involves high upfront capital costs while mFFF is a cheaper process. Moreover, the mFFF process has the advantage of potentially being exported for production in microgravity or weightless environments for in-space use. Nevertheless, most scientific literature shows comparisons of the Fused Filament Fabrication (FFF) printing stage with other AM technologies, and there are no comparative LCA "Candle to Gate" studies with mFFF processes to manufacture the same metal sample. Therefore, both MAM processes are analyzed with the LCA "Candle to Gate" method, from the extraction of raw materials to the production of the finished titanium alloy sample. The main results demonstrate a higher impact (+50%) process for mFFF and higher electrical energy consumption (7.31 kWh) compared to SLS (0.32 kWh). After power consumption, the use of titanium becomes the main contributor of Global Warming Potential (GWP) and Abiotic Depletion Potential (ADP) for both processes. Finally, an alternative scenario is evaluated in which the electrical energy is exclusively generated through photovoltaics. In this case, the results show how the mFFF process develops a more sustainable outcome than SLS.

Imaging systems and techniques for fusion-based metal additive manufacturing: a review

This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various in-situ and ex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM). In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered from in-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand, ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components.