| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
2024 Graduate Student Poster Contest
|
| Presentation Title |
SPG-12: Defining Data Quality for Aggregated Historical and Crowdsourced Additively Manufactured Material Datasets |
| Author(s) |
Ian J. Wietecha-Reiman, Todd A. Palmer |
| On-Site Speaker (Planned) |
Ian J. Wietecha-Reiman |
| Abstract Scope |
Proper data processing procedures have improved the accuracy and completeness of datasets aggregated from historical additively manufactured Ti-6Al-4V data, producing well-defined meta-data for statistical and machine learning modeling of properties such as fatigue. Unfortunately, while traditional repositories make use of heavily curated datasets, such screening and complete pedigree is not readily feasible for historical datasets. Due to the complex and nuanced nature of fatigue and metallurgical research, an index is proposed to help assess the quality of individual contributions and guide reporting practices. |