| About this Abstract |
| Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
|
| Symposium
|
Physical Modeling
|
| Presentation Title |
Inference of Metal Additive Manufacturing Process States via Deep Learning Techniques |
| Author(s) |
Richard Anarfi, Benjamin Kwapong, Kenneth Fletcher, Aaron Flood, Todd Sparks, Mugdha S. Joshi |
| On-Site Speaker (Planned) |
Mugdha S. Joshi |
| Abstract Scope |
Numerical simulation of metal additive processes are computationally intensive tasks. Iterative solution techniques for physics-based methods can lead to lengthy solution times and convergence problems, particularly if fluid dynamics of the melt pool are considered. Deep learning (DL) techniques offer an opportunity to infer solution results quickly. This paper proposes a DL method based on long short-term memory (LSTM) network trained on rendered images from metal additive process simulation and CAM data. We first obtain vector representations of the images by training on an autoencoder. LSTM is a memory-based recurrent neural network (RNN) that is capable of processing long sequences of data while combating temporal stability problems encountered with conventional RNNs. The LSTM model is used to predict images of the process given scan path and process information. This could later be used to compare with process monitoring systems as part of a quality assurance or process control schema. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |