Data-Driven Frameworks for Predictive and Prescriptive Control of Incremental Manufacturing Processes
Author(s)
Klesmith, Zoe
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Model-based approaches for manufacturing processes play a critical role in enabling first part correct production frameworks. Deep learning (DL) methods offer significant opportunities for online control and offline process design; however, there remains limited understanding regarding the fundamental applicability of such methods. This dissertation seeks to explore the suitability of deep learning-based approaches for online control and offline process design for additive manufacturing, with a particular focus on directed energy deposition and wire arc additive based processes. This work is organized in three complementary studies that explore applicability of deep learning for (1) online process monitoring and control and (2) offline process design. The first study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively. The second study explores a novel data-driven and physics-informed framework proved successful in scenarios with parameters outside of the training dataset as well as in unstable process settings such as the beginning and end of the deposition as well as during the transition between laser powers and standoff distances achieving a mean absolute percentage error of 6.51% for height and 14.89% for width. The final study creates a model that can predict layer height with an error of 0.446 mm for a test part with a wire feed speed and geometry not included in the training data. A key factor in these studies that is considered includes understanding of feasibility to be integrated onto edge devices, with implications for feedback and feed-forward control of machine platforms.
Sponsor
Date
2024-08-28
Extent
Resource Type
Text
Resource Subtype
Dissertation (PhD)