Implementation of an Ultrasonic Total Focusing Method for Inspection of Unmachined Wire+arc Additive Manufacturing Components Through Multiple Interfaces

2021 ◽  
Author(s):  
Rastislav Zimermann ◽  
Ehsan Mohseni ◽  
Randika Vithanage ◽  
David Lines ◽  
Charles Macleod ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Multiple Interfaces ◽  
Wire Arc Additive Manufacturing

In-situ dual wire arc additive manufacturing of NiTi-coating on Ti6Al4V alloys: Microstructure characterization and mechanical properties

2020 ◽  
Vol 386 ◽  
pp. 125439
Author(s):  
Jun Wang ◽  
Zengxi Pan ◽  
Long Wang ◽  
Lihong Su ◽  
Kristin Carpenter ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Microstructure Characterization ◽  
Wire Arc Additive Manufacturing

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.

Control of humping phenomenon and analyzing mechanical properties of Al–Si wire-arc additive manufacturing fabricated samples using cold metal transfer process

2021 ◽  
pp. 095440622199840
Author(s):  
Yashwant Koli ◽  
N Yuvaraj ◽  
Aravindan Sivanandam ◽  
Vipin
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Heat Input ◽  
Large Scale ◽  
High Deposition Rate ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Cold Metal Transfer ◽  
Geometrical Shapes ◽  
Wire Arc Additive Manufacturing

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.

Effect of La2O3 particle size on the microstructure and properties of Al Si alloys deposited via wire arc additive manufacturing

2021 ◽  
Vol 68 ◽  
pp. 523-533
Author(s):  
Furong Chen ◽  
Yihang Yang ◽  
Chao Chen ◽  
Qixing Wang ◽  
Ruijun Xie
Keyword(s):  
Particle Size ◽  
Additive Manufacturing ◽  
Microstructure And Properties ◽  
Wire Arc Additive Manufacturing

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Laser Hybrid Wire Arc Additive Manufacturing for Fabricating Thin Sections

2021 ◽  
Author(s):  
Shalini Singh ◽  
A. N. Jinoop ◽  
G. T. A. V. Tarun Kumar ◽  
Ashish Shukla ◽  
I. A. Palani ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Thin Sections ◽  
Laser Hybrid ◽  
Wire Arc Additive Manufacturing

Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing

2020 ◽  
Author(s):  
Raven Reisch ◽  
Tobias Hauser ◽  
Benjamin Lutz ◽  
Matteo Pantano ◽  
Tobias Kamps ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Anomaly Detection ◽  
Wire Arc Additive Manufacturing
Sign in / Sign up

Export Citation Format

Share Document