| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
In-situ Monitoring of Wire Arc Additive Manufacturing for Machine Learning Based Prediction of Shape Irregularites and Mechanical Defects |
| Author(s) |
Eduardo Miramontes, Joshua Penney, Bennett Fowler, Ethan Rummel, Sean Caufield, Anahita Khojandi, Bradley Jared |
| On-Site Speaker (Planned) |
Eduardo Miramontes |
| Abstract Scope |
Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous challenges still hindering adoption by industry. Defects in the parts degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, they can propagate to subsequent layers, causing build failure. Developing a controls framework for defect mitigation requires a model that maps undesirable outcomes to information about the process obtained in real time. The development of a multi-sensor framework for real time data acquisition and several approaches for arriving at defect prediction model, employing well known machine learning methodologies including Random Forests, and Neural Networks are explored. The models are trained first on data obtained on a single build layer, and subsequently on a multi-layer wall. Their merits and drawbacks are discussed. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |