Laser Additive Manufacturing- Direct Energy Deposition of Ti-15Mo Biomedical Alloy: Artificial Neural Network Based Modeling of Track Dilution

2020 ◽  
Vol 7 (3) ◽  
pp. 245-258 ◽  
Author(s):  
Tarun Bhardwaj ◽  
Mukul Shukla
2021 ◽  
Vol 11 (10) ◽  
pp. 4694
Author(s):  
Christian Wacker ◽  
Markus Köhler ◽  
Martin David ◽  
Franziska Aschersleben ◽  
Felix Gabriel ◽  
...  

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.


2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janmejay Dattatraya Kulkarni ◽  
Suresh Babu Goka ◽  
Pradeep Kumar Parchuri ◽  
Hajime Yamamoto ◽  
Kazuhiro Ito ◽  
...  

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.


2021 ◽  
Vol 111 (06) ◽  
pp. 368-371
Author(s):  
Sebastian Greco ◽  
Marc Schmidt ◽  
Benjamin Kirsch ◽  
Jan C. Aurich

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.


2020 ◽  
Vol 7 ◽  
pp. 6
Author(s):  
Vladimir V. Popov ◽  
Alexander Fleisher

Hybrid additive manufacturing is a relatively modern trend in the integration of different additive manufacturing techniques in the traditional manufacturing production chain. Here the AM-technique is used for producing a part on another substrate part, that is manufactured by traditional manufacturing like casting or milling. Such beneficial combination of additive and traditional manufacturing helps to overcome well-known issues, like limited maximum build size, low production rate, insufficient accuracy, and surface roughness. The current paper is devoted to the classification of different approaches in the hybrid additive manufacturing of steel components. Additional discussion is related to the benefits of Powder Bed Fusion (PBF) and Direct Energy Deposition (DED) approaches for hybrid additive manufacturing of steel components.


2020 ◽  
Vol 321 ◽  
pp. 03004
Author(s):  
Jinghao Li ◽  
Manuel Sage ◽  
Xianglin Zhou ◽  
Mathieu Brochu ◽  
Yaoyao Fiona Zhao

Metal additive manufacturing (MAM) technology is now changing the pattern of the high-end manufacturing industry, among which MAM fabricated Ti6Al4V has been far the most extensively investigated material and attracts a lot of research interests. This work established a deep neural network (DNN) to investigate the grain boundary in competitive grain growth for a bi-crystal system, the column β grains of Ti6Al4V as an example. Because of the limited number of experimental samples, the DNN is trained based on the data coming from the Geometrical Limited criterion. A series of direct energy deposition experiment using Ti6Al4V is carried out under the Taguchi experimental design. The grain boundary angles between the column grains are measured in the experiment and used to evaluate the accuracy of DNN.


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