| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Materials Design and Alloy Development III -- Super Materials and Extreme Environments
|
| Presentation Title |
Application of Taguchi, Response Surface, and Artificial Neural Networks for Rapid Optimization of Laser-based Powder-Bed Fusion Process |
| Author(s) |
Ebrahim Asadi, Behzad Fotovvati, Faridreza Attarzadeh |
| On-Site Speaker (Planned) |
Ebrahim Asadi |
| Abstract Scope |
Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), that makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V and WE43 alloys—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and and roughness parameters are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed. A multiobjective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Titanium, Magnesium |