| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling and Simulation: Microstructure, Mechanics, and Process
|
| Presentation Title |
Deep Reinforcement Learning for Defect Mitigation in Laser Powder Bed Fusion
|
| Author(s) |
Odinakachukwu Francis Ogoke, Amir Barati Farimani |
| On-Site Speaker (Planned) |
Odinakachukwu Francis Ogoke |
| Abstract Scope |
Powder-based additive manufacturing techniques enable the construction of structures that are difficult to manufacture using conventional methods. In Laser Powder Bed Fusion, components are built by selectively melting specific areas of the powder bed. However, challenges lie in the widespread adoption of these methods, due to the tendency for PBF-produced parts to develop defects and inferior physical properties in certain processing cases. Therefore, a control policy for dynamically altering process parameters to minimize the occurrence of defect causing phenomena is necessary. We present a Deep Reinforcement Learning (DRL) framework to optimize an closed-loop control strategy that avoids defect formation based on the character of the melt pool. The generated control policy alters the velocity of the laser during the melting process to improve melt pool consistency and reduce overheating. The control policy is trained on efficient simulations of the temperature behavior of the powder bed layer under various laser paths. |