| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent Quantification |
| Author(s) |
Bryer Sousa, Christopher Vieira, Rodica Neamtu, Danielle Cote |
| On-Site Speaker (Planned) |
Bryer Sousa |
| Abstract Scope |
Tacit assumptions have been made about the suitability of two primary data-driven deconvolution algorithms concerning large (10,000+) data sets captured using nanoindentation grid array measurements, including (1) probability density function determination and (2) k-means clustering and deconvolution. Recent works have found k-means clustering and probability density function fitting and deconvolution to be applicable; however, little forethought was afforded to algorithmic compatibility for nanoindentation mapping data. The present work highlights how said approaches can be applied, their limitations, the need for data pre-processing before clustering and statistical analysis, and alternatively appropriate clustering algorithms. Equally spaced apart indents (and therefore measured properties) at each recorded nanoindentation location are collectively processed via high-resolution mechanical property mapping algorithms. Clustering and mapping algorithms also explored include k-medoids, agglomerative clustering, spectral clustering, BIRCH clustering, OPTICS clustering, and DBSCAN clustering. Methods for ranking the performance of said clustering approaches against one another are also considered herein. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Mechanical Properties, Characterization |