Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data informatics: Design of Structural Materials
Presentation Title	Evaluating Uncertainty in Clustering of Nanoindentation Mapping Data
Author(s)	Bernard R. Becker, Eric D. Hintsala, Benjamin Stadnick, Douglas D. Stauffer, Ude D. Hangen
On-Site Speaker (Planned)	Eric D. Hintsala
Abstract Scope	High throughput nanoindentation mapping by nanoindentation (XPM) has potential as a technique for assisting in development of structural materials. Compared to other mechanical tests, nanoindentation is a highly localized measurement that requires minimal sample preparation and has the ability to map properties of various phases over macro length scales. Datasets of many thousands of indents can be gathered in hours, requiring a similar throughput increase in analysis. Clustering is a basic machine learning technique that has been successfully demonstrated for this task, but it’s not clear which algorithms to use, how many clusters to sort data into, and more. To begin addressing this, a quantitative comparison of clustering techniques in terms of bias and variance has been pursued by using bootstrapping of simulated datasets based upon a modelling the probability distribution of the original dataset. This technique is adaptable to evaluating other types data or other datasets as well.
Proceedings Inclusion?	Planned:
Keywords	Mechanical Properties, Characterization, High-Entropy Alloys

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