Doctoral Dissertations
Date of Award
5-2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Mechanical Engineering
Major Professor
Tony, L, Schmitz
Committee Members
Bradley Jared, Tony Shi, Michael Borish
Abstract
Additive friction stir deposition (AFSD) is a solid-state metal additive manufacturing (AM) technology, which is favored for producing near net shape, fully dense parts at a relatively high deposition rate. Although path planning methods and software currently exist for other AM processes, such as fused filament fabrication for polymers and powder bed fusion for metals, no current path planning software supports AFSD path planning. This is because in AFSD when parallel tracks are deposited the tool should be allowed to overlap the prior track, reprocessing that material into the current track and ensuring that the deposit remains fully dense and that the neighboring tracks are fully bonded. However, process feed rates must be adjusted to prevent both an overfed condition which produces excess squeeze flash and increases process reactive forces while avoiding an underfed condition which can produce galling, voids, and/or a decrease in effective layer height. These defects when introduced often cause build failure. Currently, path planning and the subsequent computer code generation to command the machine motions for AFSD are completed manually. This approach is both time-consuming, often taking longer than the material deposition time, and prone to error. Additionally, the manual approach can limit geometric complexity, which sacrifices benefits often provided by AM. To address this issue, this dissertation presents a volume-based approach to compensating process parameters to varying overlap conditions in AFSD. Allowing for the creation of an automated path planning framework which also described and subsequently demonstrated.
Recommended Citation
Kincaid, Joshua Phillip, "Automated path planning for additive friction stir deposition. " PhD diss., University of Tennessee, 2025.
https://trace.tennessee.edu/utk_graddiss/12379