About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Acoustics for In-Situ Process Data to Inform Computational Models in Metal Additive Manufacturing |
| Author(s) |
Dan J. Thoma, William Kunkel, Charlie Adkins |
| On-Site Speaker (Planned) |
Dan J. Thoma |
| Abstract Scope |
Processing–structure–property models require validated data for computational qualification of additively manufactured components. To shorten implementation cycles, in situ process monitoring provides a pathway to capture build histories and process variability. This study explores the use of in situ acoustic signatures to link processing variability to mechanical properties, with acoustics serving as surrogates for evolving microstructure. Computational models of moving heat sources are coupled to microstructure and mechanical properties and validated using experimental data across a range of process conditions. One hundred tensile specimens of equiatomic CoCrFeMnNi were fabricated using laser powder bed fusion while collecting acoustic data inside the build chamber. Incorporating acoustic features with process parameters improved prediction accuracy by 18% for yield strength and 10% for ductility compared with conventional duplicate part qualification approaches. These results demonstrate that acoustic monitoring can reduce uncertainty in mechanical property predictions despite stochastic variability. Applicability to a magnesium alloy will be demonstrated. |