Simulation and control of metal droplet transfer in bypass coupling wire arc additive manufacturing

2021 ◽  
Author(s):  
Jiankang Huang ◽  
Zhichen Guan ◽  
Shurong Yu ◽  
Xiaoquan Yu ◽  
Wen Yuan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Metal Droplet ◽  
Droplet Transfer ◽  
Simulation And Control ◽  
And Control ◽  
Wire Arc Additive Manufacturing

Characteristics of metal droplet transfer in wire-arc additive manufacturing of aluminum alloy

2018 ◽  
Vol 99 (5-8) ◽  
pp. 1521-1530 ◽  
Author(s):  
Zhu Liang ◽  
Li Jinglong ◽  
Luo Yi ◽  
Han Jingtao ◽  
Zhang Chengyang ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Additive Manufacturing ◽  
Metal Droplet ◽  
Droplet Transfer ◽  
Wire Arc Additive Manufacturing

Droplet transfer behavior in bypass-coupled wire arc additive manufacturing

2020 ◽  
Vol 49 ◽  
pp. 397-412 ◽  
Author(s):  
Jiankang Huang ◽  
Wen Yuan ◽  
Shurong Yu ◽  
Linbo Zhang ◽  
Xiaoquan Yu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Droplet Transfer ◽  
Transfer Behavior ◽  
Wire Arc Additive Manufacturing

Energy characteristics of droplet transfer in wire-arc additive manufacturing based on the analysis of arc signals

Measurement ◽  
2019 ◽  
Vol 134 ◽  
pp. 804-813 ◽  
Author(s):  
Liang Zhu ◽  
Yi Luo ◽  
Jingtao Han ◽  
Chengyang Zhang ◽  
Jie Xu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Droplet Transfer ◽  
Energy Characteristics ◽  
Wire Arc Additive Manufacturing

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.

SIMULATION AND CONTROL OF FLOODS IN A WATER NETWORK. CASE STUDY OF JIJIA RIVER CATCHMENT

2017 ◽  
Vol 16 (3) ◽  
pp. 587-595
Author(s):  
Vasile Mircea Cristea ◽  
Ph.m Thai Hoa ◽  
Mihai Mogos-Kirner ◽  
Csavdari Alexandra ◽  
Paul Serban Agachi
Keyword(s):  
Water Network ◽  
River Catchment ◽  
Simulation And Control ◽  
And Control
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