3D Printed Smart Molds for Sand Casting View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-10

AUTHORS

Jason Walker, Evan Harris, Charles Lynagh, Andrea Beck, Rich Lonardo, Brian Vuksanovich, Jerry Thiel, Kirk Rogers, Brett Conner, Eric MacDonald

ABSTRACT

Additive manufacturing, also commonly referred to as 3D printing, stands to transform sand casting with binder jetting technology that can create sand molds with unmatched geometric complexity. With printed sand molds, castings can be optimized with regard to the strength-versus-weight trade-off and structures such as periodic lattices are now available within molds that are not possible with traditional casting technology. However, an increase in design complexity invites more challenges in terms of understanding and managing both the thermodynamics and physics of the casting process. Simulations of castings are more important than ever, and empirical in situ sensor data are required to validate high fidelity computer modeling (e.g., MAGMASOFT®). One novel solution is to leverage the design freedom of CAD-based solid modeling to introduce unique mold features specifically for housing sensors (Internet of Things) within the mold to enable the collection of a diversity of data at manifold locations: temperature, pressure, moisture, gas chemistries, motion of the molds and internal cores (shifting or rotation), and magnetic field. This report describes a proof of concept in which unprecedented levels of process monitoring were integrated—both wirelessly and wired—at strategic locations throughout a printed mold and inside of internal cores. The collected data were used to validate the quality of a casting in situ as well as to provide feedback for optimizing the casting process, mold design, and simulations. A trade-off was explored between sensor survivability and disposability. More... »

PAGES

785-796

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40962-018-0211-x

DOI

http://dx.doi.org/10.1007/s40962-018-0211-x

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https://app.dimensions.ai/details/publication/pub.1101045611


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