Additive manufacturing of 3D nano-architected metals

Metal additive manufacturing (AM) processes are poised to transform the metal manufacturing industry, particularly in those areas where conventional manufacturing reaches its limitations in terms of both design freedom and manufacturing capabilities. Many metal AM systems are available today, including the powder-bed, powder-fed, and wire-fed processes based on laser, electron beam or plasma melting. At the same time, the variety of metal powder materials suitable for AM continues to expand. Currently there are 29 common metal powder materials available for AM, including stainless steels, aluminum, nickel, cobalt-chrome, and titanium alloys. The articles selected for this focus topic of JOM under Metal Powder for Additive Manufacturing are largely focused on metal powder for powder-bed fusion AM processes. The importance of metal powder characteristics in the powder-bed fusion AM processes has become increasingly recognized. How the powder flows and packs, can have a significant impact on powder bed formation, and hence the development of melt pools and microscopic homogeneity. Excessive variations in powder characteristics can lead to nonuniform layering, inconsistent bulk density, increased defects, undesired mechanical properties, and poor surface finish. As a result, it is essential to be able to identify the various powder characteristics that can ensure consistent and reliable performance, particularly when a lower cost, less spherical powder is intended for AM. In the first article, Slotwinski and Garboczi discuss the metrology needs for metal AM powders. The authors provide an informative overview of the current technical challenges and needs in characterizing metal powders for AM, processes based on laser, electron beam or plasma melting including recent efforts to standardize characterization methods in the ASTM International (ASTM) and the International Organization for Standardization (ISO), such as the recently released ASTM F3049, Standard Guide for Characterizing Properties of Metal Powders Used for Additive Manufacturing Processes. In the second article in this compilation, Clayton et al. show the necessity of appropriate metal powder characterization for AM through four case studies, and the inability of conventional characterization techniques to detect the subtle differences. These four case studies deal with (I) quantifying batch-to-batch variation in feedstocks, (II) the influence of different suppliers and manufacturing methods, (III) the effect of additives on feedstock properties, and (IV) process-relevant differences between fresh and used feedstocks. These are all important issues in metal AM. The third article by Strondl and co-workers is concerned with the characterization and control of powder properties for AM. The authors discuss the combined use of powder rheology and dynamic image analysis to characterize metal powders for AM. This study adds another useful case study to metal powder characterization for AM. In the fourth article in this sequence, Tang et al. report on the effect of powder reuse times on the AM of Ti-6Al-4V using an Arcam EBM A2 system (Arcam AB, Molndal, Sweden). Parts manufacturers are always both qualityand cost-conscious. In metal AM processes, the powder reuse times directly affect the affordability of the additively manufactured parts. Hence, it is necessary to identify the effect of powder reuse times on the AM process and the mechanical properties of the alloy thus fabricated. The powder composition, particle size distribution, apparent density, tap density, flowability, and particle morphology were studied as a function of powder reuse times and compared with respective properties of the virgin Arcam Ti-6Al-4V powder. Detailed tensile mechanical property data were produced from samples fabricated using Ti-6Al-4V powder that had been reused 16 times. The samples Ma Qian is the guest editor for the Powder Materials Committee the TMS Materials Processing and Manufacturing Division (MPMD), and coordinator of the topic Metal Powder for Additive Manufacturing (3D Printing) in this issue. JOM, Vol. 67, No. 3, 2015

Controlling melt flow by nanoparticles to eliminate surface wave induced surface fluctuation

Design Rules for Additive Manufacturing – Understanding the Fundamental Thermal Phenomena to Reduce Scrap

A numerical model-based deposition strategy for heat input regulation during plasma arc-based additive manufacturing

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the heat flux in the part. For instance, constrained heat flux because of ill-considered part design leads to defects, such as warping and thermal stress-induced cracking. Existing non-proprietary approaches to predict the heat flux in AM at the part-level predominantly use mesh-based finite element analyses that are computationally tortuous — the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational thermal models to predict the heat flux, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared to finite element analysis techniques, the proposed mesh-free graph theory-based approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer. To explore these assertions we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach, with finite element analysis and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume, and (2) simulating the layer-by-layer deposition of three part geometries in a laser powder bed fusion metal AM process with: (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the heat flux predictions from the last two approaches with a commercial solution. From the first study we report that the heat flux trend approximated by the graph theory approach is found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the heat flux trends predicted for the AM parts using graph theory approach agrees with finite element analysis with error less than 15%. More pertinently, the computational time for predicting the heat flux was significantly reduced with graph theory, for instance, in one of the AM case studies the time taken to predict the heat flux in a part was less than 3 minutes using the graph theory approach compared to over 3 hours with finite element analysis. While this paper is restricted to theoretical development and verification of the graph theory approach for heat flux prediction, our forthcoming research will focus on experimental validation through in-process sensor-based heat flux measurements.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

This article provides an overview of metal additive manufacturing (AM) processes and describes sources of failures in metal AM parts. It focuses on metal AM product failures and potential solutions related to design considerations, metallurgical characteristics, production considerations, and quality assurance. The emphasis is on the design and metallurgical aspects for the two main types of metal AM processes: powder-bed fusion (PBF) and directed-energy deposition (DED). The article also describes the processes involved in binder jet sintering, provides information on the design and fabrication sources of failure, addresses the key factors in production and quality control, and explains failure analysis of AM parts.

Sinter-based metal additive manufacturing (AM) processes that decouple the geometric shaping and consolidation of AM have several advantages over melt-based AM technologies (one-step consolidation and shaping). The sinter-based AM technologies, led by binder jetting, has the potential to significantly increase the productivity and attain more isotropic microstructures in the final product. Sinter-based AM includes the ability to cover a wide spectrum of productivity, including prototyping, low volume serial production, and high-volume mass production. Recently, several non-traditional sinter-based metal AM processes (relatively less common) have emerged. This paper will review some of these emerging and lesser-known sinter-based metal AM technologies along with a few processes based on modifications of binder jet and material extrusion techniques that have gained traction.

Additive manufacturing (AM) represents a set of manufacturing processes that create complex 3D parts out of polymers, metals, and ceramics. AM of metals and ceramics is widely used to produce parts for aerospace, automotive, and medical applications. At the micro- and nano-scales, AM is poised to become the enabling technology for efficient 3D microelectromechanical systems (MEMS), 3D micro-battery electrodes, 3D electrically small antennae, micro-optical components, and photonics. Today, the minimum feature size for most commercially available metal and ceramic AM is limited to ~20-50 μm. Currently, no established processes can reliably produce complex 3D metal and ceramic parts with sub-micron features. In this thesis, we first demonstrate a nanoscale metal AM process that can produce ~300 nm features out of nanocrystalline, nanoporous nickel using synthesized hybrid organic-inorganic materials, two-photon lithography, and pyrolysis. We study microstructure and mechanical properties of as-fabricated nickel architectures and compare their structural strength to established AM processes. We then show how this process can be extended to other metals and metalloids, including Mg, Ge, Si, and Ti. This study extends further into nanoscale AM of transparent, high refractive index materials for micro-optics and photonic crystals. We develop an AM process to 3D print fully dense nanocrystalline rutile titanium dioxide (TiO₂) with feature dimensions down to ~120 nm. We carefully study and model the relationship between feature dimensions and process parameters to achieve a Finally, a microscale AM process of titanium dioxide is demonstrated for photocatalytic water treatment. We show how synthesized hybrid organic-inorganic materials can be applied for stereolithography to print TiO₂ architectures with 100 μm features. We use the developed 3D printing process to investigate the effect of 3D architecture on the efficiency of photocatalytic water treatment. This work establishes a versatile and efficient pathway to create three-dimensional nano-architected metals and ceramics and to investigate their properties for applications in 3D MEMS, micro-optics, photonics, and photocatalysis.

This paper aims to study the energy consumption and quality characteristics of the parts fabricated by additive manufacturing (AM) technologies with a special focus on metal AM processes. AM is a family of manufacturing techniques, which is broadly used to fabricate complex and lightweight structures. The energy savings during AM processes have a significant influence on the AM industry, only if the quality of the fabricated part meets the requirements. The quality is generally represented by the surface and dimensional quality, mechanical properties, relative density, hardness, etc. The energy saving is important for environmentally benign and cleaner production, and improved product quality is useful for its application as a functional part in the aerospace, automobile, and biomedical industries. A comprehensive review of the energy consumption and quality characteristics of AM-fabricated (with special focus on metal AM) parts was carried out. Firstly, the specific energy consumption of various AM techniques has been reviewed to address the importance of energy and cleaner production. Then, the qualifications of products fabricated by different metal AM techniques have been discussed for different materials, such as titanium alloys, steel alloys, nickel alloys, and aluminum alloys. Also, by considering the practical importance of thin-walled structures fabricated by AM, a detailed analysis of their qualification has been presented. Moreover, different optimization techniques have also been reviewed for various AM process parameters and objectives. Overall, this paper provides an overview of AM, including a survey on the energy consumption and quality characteristics with the development of AM technologies for manufacturing of quality products. Finally, several future research directions are suggested, specifically the need for a framework for metal AM processes for the fabrication of quality products with minimum energy consumption.

Metal additive manufacturing (AM) processes are often energy-intensive because of the use of high-energy heat sources. Predicting energy consumption accurately is critical for optimizing AM process parameters and minimizing environmental impact. Traditional machine learning models that predict energy consumption in metal AM processes are usually not generalizable when the material or process condition varies. To address this issue, we introduce an incremental learning-integrated transfer learning (TL) approach to predict energy consumption in the directed energy deposition (DED) process. Using a small dataset collected from 20 samples fabricated with CoCrMo or IN718, we conduct three TL tasks with varying process conditions. The incremental learning approach is integrated into the source domain pre-training step to learn knowledge from small datasets more efficiently. We evaluate the performance of the extreme gradient boosting (XGBoost), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformer models. The TCN model achieves the best predictive performance with a mean absolute percentage error of 4.65%, a root mean squared error of 0.28, and a coefficient of determination of 0.92. The incremental learning-integrated TL framework achieves excellent predictive performance and generalizability with small volumes of data.

Additive Manufacturing (AM) is seen as a key enabler in the Industry 4.0 environment and therefore getting increasingly closer attention. While much research was done on the technological side in the last decades which has led to industrial maturity, economic aspects have been slightly highlighted in literature and research. To further implement metal AM and make decisions regarding economic efficiency, cost accounting and benefit analysis play a crucial role. This thesis contributes to the understanding of the economic value of AM through the analysis of sustainable benefits and the development of a novel and modular cost accounting tool for metal AM cost estimation. For benefits analysis, systematic literature research is carried out. Carbon emissions along the supply chain for production steps and transport routes are calculated concerning energy emissions. The buy-to-fly ratio is the most influential factor when it comes to the comparison of manufacturing methods. AM can save up to 70% of emissions on metal manufacturing in contrast to conventional methods considering high buy-to-fly ratios. The development of the cost accounting tool is based on several key findings of this research. First, a holistic process chain for metal AM is defined to execute and build the cost model with the method of Time-Driven Activity-Based Cost Accounting (TD-ABCA). Second, existing models in the literature are analysed and conclusions are drawn. Within the following steps, factors for geometry complexity and employee qualification are defined, economies of scale are implemented and a methodology for cost allocation in the building chamber is developed. To evaluate carbon emissions, a real use case is analysed. The digital spare part manufactured with decentral AM emits 50 % fewer carbon emissions. For the evaluation of the cost model, 28 use cases are estimated using the developed cost model and analysed regarding their cost structure. Potentials are drawn out of the results. With the further development of support-free manufacturing for laser-powder bed-based metal AM processes, up to 29 % of costs could be saved.

Metal additive manufacturing (AM) works on the principle of incremental layer-by-layer material consolidation, facilitating the fabrication of objects of arbitrary complexity through the controlled melting and resolidification of feedstock materials by using high-power energy sources. The focus of metal AM is to produce complex-shaped components made of metals and alloys to meet demands from various industrial sectors such as defense, aerospace, automotive, and biomedicine. Metal AM involves a complex interplay between multiple modes of energy and mass transfer, fluid flow, phase change, and microstructural evolution. Understanding the fundamental physics of these phenomena is a key requirement for metal AM process development and optimization. The effects of material characteristics and processing conditions on the resulting epitaxy and microstructure are of critical interest in metal AM. This article reviews various metal AM processes in the context of fabricating metal and alloy parts through epitaxial solidification, with material systems ranging from pure-metal and prealloyed to multicomponent materials. The aim is to cover the relationships between various AM processes and the resulting microstructures in these material systems.

A mixed interface-capturing/interface-tracking formulation for thermal multi-phase flows with emphasis on metal additive manufacturing processes

Metal additive manufacturing (MAM) processes have revolutionized manufacturing and design, offering unprecedented freedom to create intricate and complex parts. Research has demonstrated that in laser powder bed fusion (LPBF) of metal additive manufactured parts, the microstructure and surface can be influenced by various process parameters. However, the influence of laser pulse parameters in LPBF remains relatively unexplored. Laser pulse parameters significantly affect the microstructure and melt pool evolution in metal powder bed additive manufacturing processes. Control over these variations is crucial for achieving desired material properties and part quality. Adjustments in pulse parameters, such as power, width, and interval, can alter grain size, orientation, and subcellular structure, thus impacting mechanical properties. Moreover, optimizing laser energy density by controlling pulse parameters can mitigate defect formation, enhancing density and mechanical properties. Hence, exploring precise control over pulse width and interval during manufacturing contributes significantly to achieving high-quality components. In this study, a meso-scale numerical model was employed to investigate the influence of pulse parameters, such as pulse width and interval on the thermal history and melt pool evolution in LPBF. The physics-based model incorporates key phenomena such as heat transfer via radiation & convection, phase change, recoil pressure, and density-driven melt pool flow. These physical phenomena play a crucial role in the surface finish and microstructure of fabricated parts, affecting the formation of defects such as balling, keyhole, and spattering. A discrete element model (DEM) was employed to construct the powder bed, while the finite volume method (FVM) simulated the thermal-fluid behavior using an initial condition derived from an STL file. Validation of the numerical model against existing literature has confirmed its capacity to accurately predict melt pool behavior, including its influence on surface roughness, as well as temperature distribution and cooling rates across different laser source pulse width and interval settings. Additionally, it can also pave the way for future research directions, including the exploration of in situ hybrid processes involving multiple lasers for surface processing and enhancement. The evolution of computational models promises to facilitate more sophisticated control strategies, ultimately enhancing outcomes and efficiency in metal additive manufacturing processes, paving the way for tailored and optimized LPBF MAM parts.