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PLoS One. 2018 Apr 5;13(4):e0194890. doi: 10.1371/journal.pone.0194890. eCollection 2018.

Expert-guided optimization for 3D printing of soft and liquid materials.

Author information

1
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
3
Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
4
Santa Fe Institute, Santa Fe, New Mexico, United States of America.
5
Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Abstract

Additive manufacturing (AM) has rapidly emerged as a disruptive technology to build mechanical parts, enabling increased design complexity, low-cost customization and an ever-increasing range of materials. Yet these capabilities have also created an immense challenge in optimizing the large number of process parameters in order achieve a high-performance part. This is especially true for AM of soft, deformable materials and for liquid-like resins that require experimental printing methods. Here, we developed an expert-guided optimization (EGO) strategy to provide structure in exploring and improving the 3D printing of liquid polydimethylsiloxane (PDMS) elastomer resin. EGO uses three steps, starting first with expert screening to select the parameter space, factors, and factor levels. Second is a hill-climbing algorithm to search the parameter space defined by the expert for the best set of parameters. Third is expert decision making to try new factors or a new parameter space to improve on the best current solution. We applied the algorithm to two calibration objects, a hollow cylinder and a five-sided hollow cube that were evaluated based on a multi-factor scoring system. The optimum print settings were then used to print complex PDMS and epoxy 3D objects, including a twisted vase, water drop, toe, and ear, at a level of detail and fidelity previously not obtained.

PMID:
29621286
PMCID:
PMC5886457
DOI:
10.1371/journal.pone.0194890
[Indexed for MEDLINE]
Free PMC Article

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