| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Materials Design and Alloy Development II
|
| Presentation Title |
Efficient Material and Processing Parameter Optimization in Laser Powder Bed Fusion Through Novel Amalgamation of Computational Modeling, Non-destructive Evaluation, and Material Characterization |
| Author(s) |
Christopher Peitsch, Steven Storck, Ian McCue, Joseph Sopcisak, Morgan Trexler |
| On-Site Speaker (Planned) |
Christopher Peitsch |
| Abstract Scope |
Current standards and best practices for material qualification in additive manufacturing (AM) often constitute an expensive process that can inhibit rapid development of new materials and structures. Intelligent experimental design, derived from computational modeling, can be used to bound the selection of candidate material alloys and processing parameters. By developing custom sample geometries, traditionally costly characterization techniques, such as x-ray computed tomography, can be tailored to improve efficiency. This technique is also complemented with other fast and inexpensive characterization methods such as Vickers hardness or compression testing. The combination of these data, with modeling and non-destructive analysis can be used to evaluate defects in the resulting structures, and enable rapid deconvolution of the material-process-performance relationships. The results of this technique have been shown to improve the overall development of special alloys for AM, including metal matrix composites and shape memory alloys, reducing the time invested from weeks to days. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |