| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Cast Shop Technology
|
| Presentation Title |
Automated Metal Cleanliness Analyzer (AMCA): Digital Image Analysis Phase Differentiation and Benchmarking Against PoDFA-derived Cleanliness Data |
| Author(s) |
Hannes Zedel, Robert Fritzsch, Ragnhild Elizabeth Aune, Shahid Akhtar |
| On-Site Speaker (Planned) |
Hannes Zedel |
| Abstract Scope |
Assessing metal cleanliness of aluminum melts is critical for product quality control, as well as for process optimization. PoDFA is the current standard method for assessing aluminum cleanliness but has limitations in speed and costs due to its manual image processing. The Automated Metal Cleanliness Analyzer (AMCA) method was previously demonstrated to produce cleanliness indicators highly correlating to the main cleanliness indicator of industrial PoDFA analyses on the same samples. In the present work, the features of the AMCA method were expanded, introducing quantitative inclusion characterization and enhanced detection features. The results were systematically benchmarked against industrial PoDFA-derived cleanliness data. The results confirm the equivalence of the total particle area and provide moderate differentiation of inclusion types. Thereby, AMCA shows potential to be used as an alternative to PoDFA, deriving cleanliness data of aluminum samples for generating extensive process data at superior cost-scaling and minimized human biases. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Characterization, Computational Materials Science & Engineering |