scholarly journals Influence of Metallic Powder Characteristics on Extruded Feedstock Performance for Indirect Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7136
Author(s):  
Cyril Santos ◽  
Daniel Gatões ◽  
Fábio Cerejo ◽  
Maria Teresa Vieira
Keyword(s):  
Additive Manufacturing ◽  
Electron Beam Melting ◽  
Metallic Powder ◽  
Micro Ct ◽  
Direct Energy Deposition ◽  
3D Objects ◽  
Direct Energy ◽  
Powder Characteristics ◽  
Manufacturing Technologies

Material extrusion (MEX) of metallic powder-based filaments has shown great potential as an additive manufacturing (AM) technology. MEX provides an easy solution as an alternative to direct additive manufacturing technologies (e.g., Selective Laser Melting, Electron Beam Melting, Direct Energy Deposition) for problematic metallic powders such as copper, essential due to its reflectivity and thermal conductivity. MEX, an indirect AM technology, consists of five steps—optimisation of mixing of metal powder, binder, and additives (feedstock); filament production; shaping from strands; debinding; sintering. The great challenge in MEX is, undoubtedly, filament manufacturing for optimal green density, and consequently the best sintered properties. The filament, to be extrudable, must accomplish at optimal powder volume concentration (CPVC) with good rheological performance, flexibility, and stiffness. In this study, a feedstock composition (similar binder, additives, and CPVC; 61 vol. %) of copper powder with three different particle powder characteristics was selected in order to highlight their role in the final product. The quality of the filaments, strands, and 3D objects was analysed by micro-CT, highlighting the influence of the different powder characteristics on the homogeneity and defects of the greens; sintered quality was also analysed regarding microstructure and hardness. The filament based on particles powder with D50 close to 11 µm, and straight distribution of particles size showed the best homogeneity and the lowest defects.

scholarly journals Influence of Metallic Powder Characteristics On Extruded Feedstock Performance For Indirect Additive Manufacturing

2021 ◽  
Author(s):  
Cyril Santos ◽  
Daniel Gatões ◽  
Fábio Cerejo ◽  
Teresa Vieira
Keyword(s):  
Additive Manufacturing ◽  
Volume Concentration ◽  
Metallic Powder ◽  
Main Role ◽  
Micro Ct ◽  
Powder Mixing ◽  
3D Objects ◽  
Powder Characteristics ◽  
Manufacturing Technologies

Abstract Material Extrusion (MEX) of metallic powder-based filaments has been showing great potential as an additive manufacturing technology. MEX provides an easy solution, as an alternative to direct additive manufacturing technologies (e.g. SLM, EBM, DED) for problematic metallic powder, like copper powder, due to its reflectivity and thermal conductivity. MEX, an indirect technology, consists of 5 steps – optimising metal powder, mixing (feedstock), filament production, shaping, debinding and sintering. The great challenge in MEX is, undoubtedly, filament manufacturing for optimal green density, and consequently the sintered properties. The filament, to be extrudable, must accomplish at optimal powder volume concentration (CPVC) with good rheological performance, flexibility, and stiffness. These have a main role in the quality of the 3D objects after debinding and sintering. In this study, a feedstock composition (similar binder, additives and CPVC - 61% vol.) of copper with three different particle powder characteristics was selected, in order to highlight their role. The quality of the filaments, strands and 3D objects was analyzed by micro-CT highlighting the influence of the different powder characteristics on the homogeneity and defects of the greens. Sintered filaments were also analysed, regarding hardness, microstructure and a comparison between green and sintered defects, using micro-CT. The filament based on particles powder with D50 close to 11 µm and straight distribution of particles size shows the best homogeneity and lower defects.

scholarly journals Sensor-Based Additive Manufacturing Technologies

2021 ◽  
Vol 12 (3) ◽  
pp. 3513-3521
Keyword(s):  
Additive Manufacturing ◽  
Wearable Sensors ◽  
Sensor Technology ◽  
Layer By Layer ◽  
Environmental Sensors ◽  
Direct Energy Deposition ◽  
Powder Bed ◽  
Direct Energy ◽  
Building Components ◽  
Manufacturing Technologies

Additive manufacturing is the term that uses the CAD data to build components layer by layer; it is also termed layered manufacturing or 3D printing. The major advantage of additive manufacturing is the capability of building components without the use of molds or tools. Five major categories of AM processes include Powder Bed Fusion (PBF), Direct Energy Deposition (DED), Material Jetting (MJ), Binder Jetting (BJ), and Sheet Lamination (SL). The sensor may be defined as a device that responds to a physical stimulus and transmits a resulting impulse. Sensor technology has been widely adopted in advanced manufacturing, aerospace, biomedical and robotic applications. Commonly used sensors are temperature sensors, strain sensors, biosensors, environmental sensors, and wearable sensors, etc. Additive manufacturing technologies can fabricate sensors and microfluidic devices with less labor. This paper focuses on various sensors developed by additive manufacturing processes, and their practical application for the particular purpose is reviewed.

scholarly journals Geometry and Distortion Prediction of Multiple Layers for Wire Arc Additive Manufacturing with Artificial Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4694
Author(s):  
Christian Wacker ◽  
Markus Köhler ◽  
Martin David ◽  
Franziska Aschersleben ◽  
Felix Gabriel ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Welding Process ◽  
High Energy ◽  
Welding Distortion ◽  
Direct Energy Deposition ◽  
Direct Energy ◽  
Industrial Use ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Wire Arc Additive Manufacturing

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality.

Microstructure evolution along build direction for thin-wall components fabricated with wire-direct energy deposition

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janmejay Dattatraya Kulkarni ◽  
Suresh Babu Goka ◽  
Pradeep Kumar Parchuri ◽  
Hajime Yamamoto ◽  
Kazuhiro Ito ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Microstructural Evolution ◽  
Microstructure Evolution ◽  
Energy Deposition ◽  
Direct Energy Deposition ◽  
Content Type ◽  
Layer Deposition ◽  
Different Types ◽  
Direct Energy ◽  
Solidification Phenomenon

Purpose The use of a gas metal arc welding-based weld-deposition, referred to as wire-direct energy deposition or wire-arc additive manufacturing, is one of the notable additive manufacturing methods for producing metallic components at high deposition rates. In this method, the near-net shape is manufactured through layer-by-layer weld-deposition on a substrate. However, as a result of this sequential weld-deposition, different layers are subjected to different types of thermal cycles and partial re-melting. The resulting microstructural evolution of the material may not be uniform. Hence, the purpose of this study is to assess microstructure variation along with the lamination direction (or build direction). Design/methodology/approach The study was carried out for two different boundary conditions, namely, isolated condition and cooled condition. The microstructural evolution across the layers is hypothesized based on experimental assessment; this included microhardness, scanning electron microscopy imaging and electron backscatter diffraction analysis. These conditions subsequently collaborated with the help of thermal modeling of the process. Findings During a new layer deposition, the previous layer also is subject to re-melt. While the newly added layer undergoes rapid cooling through a combination of convection, conduction and radiation losses, the penultimate layer, sees a slower cooling curve due to its smaller exposure area. This behavior of rapid-solidification and subsequent re-melting and re-solidification is a progressing phenomenon across the layers and the bulk of the layers have uniform grains due to this remelt-re-solidification phenomenon. Research limitations/implications This paper studies the microstructure variation along with the build direction for thin-walled components fabricated through weld-deposition. This study would be helpful in addressing the issue of anisotropy resulting from the distinctive thermal history of each layer in the overall theme of metal additive manufacturing. Originality/value The unique aspect of this paper is the postulation of a generic hypothesis, based on experimental findings and supported by thermal modeling of the process, for remelt-re-solidification phenomenon followed by temperature raising/lowering repetitively in every layer deposition across the layers. This is implemented for different types of base plate conditions, revealing the role of boundary conditions on the microstructure evolution.

Anwendungszentrum für additive Fertigung/Center for Applied Additive Manufacturing – Influence of process parameters during high-speed laser direct energy deposition

2021 ◽  
Vol 111 (06) ◽  
pp. 368-371
Author(s):  
Sebastian Greco ◽  
Marc Schmidt ◽  
Benjamin Kirsch ◽  
Jan C. Aurich
Keyword(s):  
Additive Manufacturing ◽  
High Speed ◽  
Energy Deposition ◽  
Influence Of Process Parameters ◽  
Direct Energy Deposition ◽  
Additive Fertigung ◽  
Powder Bed ◽  
High Productivity ◽  
Direct Energy ◽  
Near Net Shape

Additive Fertigungsverfahren zeichnen sich durch die Möglichkeit der endkonturnahen Fertigung komplexer Geometrien aus. Die geringe Produktivität etablierter Verfahren wie etwa dem Pulverbettverfahren hemmen aktuell den wirtschaftlichen Einsatz additiver Fertigung. Das Hochgeschwindigkeits-Laserauftragschweißen (HLA) soll durch deutlich erhöhte Auftragsraten und somit bisher unerreicht hoher Produktivität bei der additiven Fertigung dazu beitragen, deren Wirtschaftlichkeit zu steigern.   Additive manufacturing enables the near-net-shape production of complex geometries. The low productivity of established processes such as powder bed processes is currently limiting the economic use of additive manufacturing. High-speed laser direct energy deposition (HS LDED) is expected to improve the economic efficiency of additive manufacturing by significantly increasing deposition rates and thus previously unattained high productivity.

Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements

2020 ◽  
Author(s):  
Khalil Al Handawi ◽  
Petter Andersson ◽  
Massimo Panarotto ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Optimal Designs ◽  
Design Problems ◽  
Direct Energy Deposition ◽  
Engineering Design Problems ◽  
Direct Energy ◽  
Transition Rules ◽  
Set Based Design ◽  
Manufacturing Technologies ◽  
Process Solutions

Abstract Engineering design problems often have open-ended requirements, especially in the early stages of development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.

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