| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Standards for Data Science in Additive Manufacturing
|
| Presentation Title |
Transferability of Workflow in Direct Ink Write Printing and Analysis |
| Author(s) |
Hein Htet Aung, Balashanmuga Priyan Rajamohan, Quynh D. Tran, Jayvic Cristian Jimenez, Brian Au, Robert Cerda, Pigeon Caviness, Brian Giera, Roger H. French, Laura S. Bruckman |
| On-Site Speaker (Planned) |
Hein Htet Aung |
| Abstract Scope |
The reproducibility of high-quality parts is paramount to ensuring the scalability of Advanced Manufacturing (AM) processes. AM processes can often be complex, involving many process parameters and hardware components. With advancements in high-throughput experimentation, data streams generated from the printing process are also increasing in diversity and volume. In addition, changes in data collection standards, instruments, and operators are common across different organizations. These complex print processes, large data streams, and varying data collection standards challenge the transferability of workflow in printing and data analysis. Thus, the lack of transferability inhibits the reproducibility of high-quality parts. Establishing a comprehensive study protocol guided by FAIR principles that standardize data collection, processing, and analysis procedures aids in workflow transferability. In this work, we showcase the transferability of workflow and analysis on two different Direct Ink Write mechatronics datasets obtained five years apart.
This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the U.S. Department of Energy's National Nuclear Security Administration under Award Number(s) DE-NA0004104 and partially under the auspices of Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, LLNL-ABS-863943 |