| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Understanding Variation in the Additive Manufacturing Supply Chain for Improved Modeling Performance |
| Author(s) |
Donald Godfrey |
| On-Site Speaker (Planned) |
Donald Godfrey |
| Abstract Scope |
In the current government acceptance criterion environment, there is a heavy burden on “Performance Based Data”. However, “Performance Based Data” cost hundreds-of-thousands of dollars and many times exceed one million dollars for each alloy to achieve ”B-Basis” . The current lack of material property data can be addressed by developing modeling software that accurately predict material properties. A barrier to adoption of AM is the need to qualify parts on a range of machines across a number of vendors, and the cost of qualifying each machine type or sometimes even machine serial number through qualification builds and testing is burdensome. Using better process monitoring, advancing NDE and coupling that to ICME could allow for better generic material data sets. This presentation will focus on the variances of an additive manufacturing supply-chain and the need to minimize these variables for better more accurate modeling and predictability. |
| Proceedings Inclusion? |
Undecided |