| About this Abstract |
| Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
|
| Symposium
|
Plenary
|
| Presentation Title |
On Corrections of Global Shifts in Metal Additive Manufacturing |
| Author(s) |
Subhrajit Roychowdhury, Sharath Aramanekoppa, Rajesh Bollapragada, Brion Sarachan, Bakshi Kaivalya, Rogier Blom |
| On-Site Speaker (Planned) |
Subhrajit Roychowdhury |
| Abstract Scope |
In the current paradigm of metal additive manufacturing, the process runs open loop with pre-specified process parameters. It is therefore important to ensure that the machine is delivering the commanded parameters under all conditions. However, due to various factors, viz., variations in ambient temperature, thermal lensing, optics misalignment, optics degradation, laser degradation etc., the process parameters at the powder bed may drift from the commanded ones. To maintain process compliance, we developed a machine learning based algorithm that can estimate the deviations between commanded and delivered power and spot-size based on the photodiode data from a witness coupon on a layer wise basis. To compensate for the drifts that typically acts over a slow time scale (hour to days), we implemented a layer-wise compensation mechanism based on the estimates to ensure desired power and spot-size is delivered to the process. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |