Comparative Evaluation of Machine Learning Regressors for the Layer Geometry Prediction in Wire arc Additive manufacturing

2021 ◽  
Author(s):  
German Omar Barrionuevo ◽  
Sergio Rios ◽  
Stewart W. Williams ◽  
Jorge Andres Ramos-Grez
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Comparative Evaluation ◽  
Wire Arc Additive Manufacturing

Machine Learning Models for Detecting Weld Bead Defects in Wire-Arc Additive Manufacturing

2021 ◽  
Vol 26 (2) ◽  
pp. 131-143
Author(s):  
Jun-Seong Kim ◽  
Gi-Jeong Seo ◽  
Duck Bong Kim ◽  
Jong-Ho Shin ◽  
Hyungjun Park
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Weld Bead ◽  
Learning Models ◽  
Wire Arc Additive Manufacturing ◽  
Machine Learning Models

scholarly journals Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Xinyi Xiao ◽  
Clarke Waddell ◽  
Carter Hamilton ◽  
Hanbin Xiao
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Prediction Model ◽  
Analytical Models ◽  
Process Variables ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Learning Framework ◽  
And Control ◽  
Wire Arc Additive Manufacturing

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

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.

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