Model-based deep learning for additive manufacturing: New frontiers and applications

被引：6

作者：

Ghungrad, Suyog ^{[1
]}

Gould, Benjamin ^{[2
]}

Soltanalian, Mojtaba ^{[3
]}

Wolff, Sarah Jeannette ^{[4
]}

Haghighi, Azadeh ^{[1
]}

机构：

[1] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA

[2] Argonne Natl Lab, Appl Mat Div, 9700 S Cass Ave, Lemont, IL 60439 USA

[3] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA

[4] Texas A&M Univ, Ind & Syst Engn, College Stn, TX 77843 USA

来源：

MANUFACTURING LETTERS | 2021年 / 29卷

关键词：

Additive manufacturing; Model-based deep learning; Artificial intelligence; Data scarcity; Process and material variability; TOPOLOGY OPTIMIZATION; DESIGN;

D O I：

10.1016/j.mfglet.2021.07.008

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Artificial intelligence has created disruptive possibilities in additive manufacturing towards smarter design, process control, and quality assurance. Nonetheless, the scarcity of data in additive manufacturing significantly limits the wide adoption of artificial intelligent techniques. In this work, we propose the deployment of a novel artificial intelligent structure called model-based deep learning in the context of additive manufacturing which can address scenarios with scarce data but an available underlying iterative mathematical/inference model. Several immediate applications of this technique in the additive manufacturing research as well as a proof of concept on temperature profile prediction in metal AM process are presented. (C) 2021 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.

引用

页码：94 / 98

页数：5

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