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.

scholarly journals Direct Energy Deposition of TiAl for Hybrid Manufacturing and Repair of Turbine Blades

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4392
Author(s):  
Silja-Katharina Rittinghaus ◽  
Janett Schmelzer ◽  
Marcus Willi Rackel ◽  
Susanne Hemes ◽  
Andreas Vogelpoth ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Energy Deposition ◽  
Phase Distribution ◽  
Turbine Blades ◽  
High Energy ◽  
Hybrid Manufacturing ◽  
Chemical Compositions ◽  
Compression Tests ◽  
Direct Energy Deposition ◽  
Direct Energy

While repair is mainly used to restore the original part geometry and properties, hybrid manufacturing aims to exploit the benefits of each respective manufacturing process regarding either processing itself or resulting part characteristics. Especially with the current implementation of additive manufacturing in the production of TiAl, turbine blades for both hybrid manufacturing and repair new opportunities are enabled. One main issue is the compatibility of the two or more material types involved, which either differ regarding composition or microstructure or both. In this study, a TNMTM-alloy (Ti-Nb-Mo) was manufactured by different processes (casting, forging, laser additive manufacturing) and identically heat-treated at 1290 °C. Chemical compositions, especially aluminum and oxygen contents, were measured, and the resulting microstructures were analyzed with Scanning Electron Microscopy (SEM) and High-energy X-ray diffraction (HEXRD). The properties were determined by hardness measurements and high-temperature compression tests. The comparison led to an overall assessment of the theoretical compatibility. Experiments to combine several processes were performed to evaluate the practical feasibility. Despite obvious differences in the final phase distribution caused by deviations in the chemical composition, the measured properties of the samples did not differ significantly. The feasibility of combining direct energy deposition (DED) with either casting or laser powder bed fusion (LPBF) was demonstrated by the successful build of the dense, crack-free hybrid material.

Wire + Arc Additive Manufacturing of Metals

2020 ◽  
pp. 106-126
Author(s):  
Krishna Kishore Mugada ◽  
Aravindan Sivanandam ◽  
Ravi Kumar Digavalli
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Manufacturing Sector ◽  
Manufacturing Processes ◽  
Deposition Rates ◽  
Direct Energy Deposition ◽  
Powder Bed ◽  
Direct Energy ◽  
Wire Arc Additive Manufacturing ◽  
Binder Jetting

Wire + Arc additive manufacturing (WAAM) processes have become popular because of their proven capabilities to produce large metallic components with high deposition rates (promoted by arc-based processes) compared to conventional additive manufacturing processes such as powder bed fusion, binder jetting, direct energy deposition, etc. The applications of WAAM processes were constantly increasing in the manufacturing sector, which necessitates an understanding of the process capability to various metals. This chapter outlines the significant outcomes of the WAAM process for most of the engineering metals in terms of microstructure and mechanical properties. Discussion on various defects associated with the processed components is also presented. Potential application of WAAM for different metals such as aluminum and its alloys, titanium, and steels was discussed. The research indicates that the components manufactured by the WAAM process have significant microstructural changes and improved mechanical properties.

scholarly journals The Corrosion Behavior of 316L Stainless Steel Additively Manufactured by Direct Energy Deposition Process

2021 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Tomer Ron ◽  
Avi Leon ◽  
Amnon Shirizly ◽  
Eli Aghion
Keyword(s):  
Stainless Steel ◽  
Corrosion Resistance ◽  
Additive Manufacturing ◽  
Corrosion Behavior ◽  
Energy Deposition ◽  
316L Stainless Steel ◽  
High Energy ◽  
Aisi 316L ◽  
Direct Energy Deposition ◽  
Direct Energy

Traditional additive manufacturing (AM) technologies tend to focus on powder bed fusion (PBF) methods, such as SLM (selective laser melting) and EBM (electron beam melting), that are attractive for the rapid production of complex components. However, their inherent drawbacks include the high cost of powders, high energy consumption and size limitation. Hence, more affordable and flexible direct energy deposition processes, such as wire arc additive manufacturing (WAAM), are gaining increased interest. This study aims to evaluate the corrosion behavior, including the stress corrosion resistance of 316L stainless steel, produced by the WAAM process. Experimental samples in the form of cylindrical rods were produced by WAAM process using 316L stainless steel wires and compared with their counterpart AISI 316L alloy. The corrosion resistance was evaluated using potentiodynamic polarization, impedance spectroscopy and slow strain rate testing (SSRT). Despite the differences between the microstructures of printed WAAM 316L alloy and its counterpart AISI 316L, the corrosion performance of both alloys in 3.5% NaCl solution was quite similar.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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.

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