Author ORCID Identifier
0000-0001-7669-0202
Defense Date
2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Mechanical and Nuclear Engineering
First Advisor
Ravi Hadimani
Abstract
Collaborative robots have the potential to transform the world, being easier to program, safer around humans, and far cheaper than their industrial counterparts. This makes them uniquely qualified to operate autonomously in environments where the presence of humans is necessary, either as an operator themselves or as observers of active processes. One of the fields where collaborative robots have significant opportunity for utility is in metal additive manufacturing, in which near net-shape parts can be autonomously fabricated using materials like stainless steel, titanium, or nickel super-alloys. These processes, especially laser powder bed fusion and directed energy deposition, use a fine powder feedstock to incrementally print incredibly complex metal parts; however, this powder has the downside in that it poses a severe hazard to any humans unfortunate enough to exist unprotected around it, damaging eyes and lungs. This risk is maximized once the part is fully printed and is being taken out of the machine, where all the leftover powder must be removed prior to "post-processing," which includes any additional processes like heat treatment, surface finishing, and support removal. Currently, this is facilitated almost entirely by humans, despite the hazards, simply because most machines are not capable of automating these steps. This leads to my research, which investigates the development of a framework providing the fundamental tools required for automation. The ubiquity of additive manufacturing is precisely why automation is so challenging; unlike in traditional manufacturing, where the inputs and outputs of the processes are incredibly consistent, additive processes are typically much lower throughput and more varied, so automation solutions must be both (1) efficient, such that implementations of automation are accessible, and (2) flexible and robust enough to handle the intrinsic variation of additive technologies. Paramount to this is the development of the end effectors required for collaborative robots to effectively interact with these parts, the design of an efficient, powerful computer 3D vision algorithm, and the culmination of a significant aspect of the post-processing workflow in a powder cleanup routine. These developments will prove foundational to further expansion of automation within an additive manufacturing workflow, with collaborative robots leading the way as a critical enabler.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
12-13-2025