Bridging the Oldest and Newest Manufacturing Technologies: Additive Manufacturing, Metal Casting, and Internet of Things
Restricted (Penn State Only)
- Author:
- King, Philip
- Graduate Program:
- Mechanical Engineering (PHD)
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 07, 2023
- Committee Members:
- Robert Kunz, Professor in Charge/Director of Graduate Studies
Robert Kunz, Major Field Member
Guhaprasanna Manogharan, Chair & Dissertation Advisor
Andrea Arguelles, Outside Unit & Field Member
Robert Voigt, Major Field Member - Keywords:
- Additive Manufacturing
Hybrid Manufacturing
Design for Additive Manufacturing
Metal Casting - Abstract:
- Metal casting is an old but important manufacturing technology, with an estimated 90% of durable manufactured products containing a casting. While traditional metal casting has existed for thousands of years, it remains a complex process under controlled conditions that suffers from variable casting outcomes, where the same casting design can have on casting run be defect free, and another casting run that must be scrapped. This variability has long been considered unavoidable, and even acceptable by the metal casting community. However, this outlook is no longer feasible, as nations across the world try to improve sustainability and reduce energy consumption. This dissertation presents original research to improve the performance of sand casting, the most widely used metal casting process, so that the industry can improve its sustainability. Leveraging additive manufacturing (AM) via 3D sand-printing enables us to reimagine the design principles of 3D mold geometry to improve casting quality and reduce casting variation. In this research, 3D sprue geometries were systematically studied for the naval alloy nickel aluminum bronze (NAB). When compared to the straight sprue geometry traditionally used in industry, the optimized 3D sprue geometries increased the ultimate flexural strength by 28% with while also reducing the variation with statistical significance. In addition to 3D sprue geometries, this dissertation studied 3D runner geometries for both AM and traditional molds. The optimized 3D runner geometries improved casting consistency by reducing the variance of the ultimate flexural strength by a statistically significant 74%. To accelerate our understanding of melt filling in sand molds, this dissertation also investigated new process monitoring techniques that can be integrated with IoT sensors (internet-of-things) with the potential to rapidly identify defective castings. Computer vision tracked the melt head in the runner system of open molds to extract its velocity, while ultrasonic sensors provided a means to measure in-gate velocity that previously could not be measured. This novel process measured the liquid metal surface height as the mold filled and then calculated the in-gate velocity using the surface height data. Overall, the innovations presented in the dissertation have made original contributions to the science of 3D mold design via 3D sand-printing to improve casting performance and in-process monitoring capabilities.