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Title: Data-Driven Predictive Modeling of Tensile Behavior of Parts Fabricated by Cooperative 3D Printing
Abstract 3D printing has been extensively used for rapid prototyping as well as low-volume production in aerospace, automotive, and medical industries. However, conventional manufacturing processes (i.e., injection molding and CNC machining) are more economical than 3D printing for high-volume mass production. In addition, current 3D printing techniques are not capable of fabricating large components due to the limited build size of commercially available 3D printers. To increase 3D printing throughput and build volume, a novel cooperative 3D printing technique has been recently introduced. Cooperative 3D printing is an additive manufacturing process where individual mobile 3D printers collaborate on printing a part simultaneously, thereby increasing printing speed and build volume. While cooperative 3D printing has the potential to fabricate larger components more efficiently, the mechanical properties of the components fabricated by cooperative 3D printing have not been systematically characterized. This paper aims to develop a data-driven predictive model that predicts the tensile strength of the components fabricated by cooperative 3D printing. Experimental results have shown that the predictive model is capable of predicting tensile strength as well as identifying the significant factors that affect the tensile strength.  more » « less
Award ID(s):
1914249
PAR ID:
10209030
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of Computing and Information Science in Engineering
Volume:
20
Issue:
2
ISSN:
1530-9827
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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