scholarly journals A Review of Aluminum Alloy Fabricated by Different Processes of Wire Arc Additive Manufacturing

2021 ◽  
Vol 27 (1) ◽  
pp. 18-26
Author(s):  
Zeli WANG ◽  
Yuanbin ZHANG

Due to the advantages of high deposition rate, low equipment cost and high material utilization rate, aluminum alloy fabricated by wire arc additive manufacturing (WAAM) has been widely concerned by scholars and scientific institutions. This article reviews the features of aluminum alloy fabricated by different WAAM processes (GMAW-based, GTAW-based, CMT-based, PAW-based). Research status of the porosity, dimensional accuracy, microstructure and mechanical properties of aluminum alloy in different process is analyzed. General tendency of microstructure and mechanical properties were discussed, and suggestions for the future research direction were put forward including mandatory constraint on molten pool and structural topological optimization.

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4187
Author(s):  
Shalini Singh ◽  
Arackal Narayanan Jinoop ◽  
Gorlea Thrinadh Ananthvenkata Tarun Kumar ◽  
Iyamperumal Anand Palani ◽  
Christ Prakash Paul ◽  
...  

Wire arc additive manufacturing is a metal additive manufacturing technique that allows the fabrication of large size components at a high deposition rate. During wire arc additive manufacturing, multi-layer deposition results in heat accumulation, which raises the preheat temperature of the previously built layer. This causes process instabilities, resulting in deviations from the desired dimensions and variations in material properties. In the present study, a systematic investigation is carried out by varying the interlayer delay from 20 to 80 s during wire arc additive manufacturing deposition of the wall structure. The effect of the interlayer delay on the density, geometry, microstructure and mechanical properties is investigated. An improvement in density, reduction in wall width and wall height and grain refinement are observed with an increase in the interlayer delay. The grain refinement results in an improvement in the micro-hardness and compression strength of the wall structure. In order to understand the effect of interlayer delay on the temperature distribution, numerical simulation is carried out and it is observed that the preheat temperature reduced with an increase in interlayer delay resulting in variation in geometry, microstructure and mechanical properties. The study paves the direction for tailoring the properties of wire arc additive manufacturing-built wall structures by controlling the interlayer delay period.


Author(s):  
Yashwant Koli ◽  
N Yuvaraj ◽  
Aravindan Sivanandam ◽  
Vipin

Nowadays, rapid prototyping is an emerging trend that is followed by industries and auto sector on a large scale which produces intricate geometrical shapes for industrial applications. The wire arc additive manufacturing (WAAM) technique produces large scale industrial products which having intricate geometrical shapes, which is fabricated by layer by layer metal deposition. In this paper, the CMT technique is used to fabricate single-walled WAAM samples. CMT has a high deposition rate, lower thermal heat input and high cladding efficiency characteristics. Humping is a common defect encountered in the WAAM method which not only deteriorates the bead geometry/weld aesthetics but also limits the positional capability in the process. Humping defect also plays a vital role in the reduction of hardness and tensile strength of the fabricated WAAM sample. The humping defect can be controlled by using low heat input parameters which ultimately improves the mechanical properties of WAAM samples. Two types of path planning directions namely uni-directional and bi-directional are adopted in this paper. Results show that the optimum WAAM sample can be achieved by adopting a bi-directional strategy and operating with lower heat input process parameters. This avoids both material wastage and humping defect of the fabricated samples.


Metals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 892 ◽  
Author(s):  
Eider Aldalur ◽  
Fernando Veiga ◽  
Alfredo Suárez ◽  
Jon Bilbao ◽  
Aitzol Lamikiz

Additive manufacturing has gained relevance in recent decades as an alternative to the manufacture of metal parts. Among the additive technologies, those that are classified as Directed Energy Deposition (DED) are characterized by their high deposition rate, noticeably, Wire Arc Additive Manufacturing (WAAM). However, having the inability to produce parts with acceptable final surface quality and high geometric precision is to be considered an important disadvantage in this process. In this paper, different torch trajectory strategies (oscillatory motion and overlap) in the fabrication of low carbon steel walls will be compared using Gas Metal Arc Welding (GMAW)-based WAAM technology. The comparison is done with a study of the mechanical and microstructural characteristics of the produced walls and finally, addressing the productivity obtained utilizing each strategy. The oscillation strategy shows better results, regarding the utilization rate of deposited material and the flatness of the upper surface, this being advantageous for subsequent machining steps.


2021 ◽  
Author(s):  
Chunyang Xia ◽  
Zengxi Pan ◽  
Yuxing Li ◽  
Huijun Li

Abstract Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, and robot suspend. this study proposed to apply Deep Learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.


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