2024-10-12-howland24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss	Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards an end-to-end deep learning system for accelerating advanced manufacturing research and development.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	howland24a	0	Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss	202	211	202-211	202	false	Howland, Sylvia and Kappagantula, Keerti-Sahithi and Kvinge, Henry and Emerson, Tegan	given family Sylvia Howland given family Keerti-Sahithi Kappagantula given family Henry Kvinge given family Tegan Emerson	2024-10-12		Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM)	251	inproceedings	date-parts 2024 10 12	https://raw.githubusercontent.com/mlresearch/v251/main/assets/howland24a/howland24a.pdf