| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Additive Manufacturing: Alloy Design to Develop New Feedstock Materials
|
| Presentation Title |
Application of Taguchi, Response Surface, and Artificial Neural Networks for Rapid Optimization of Direct Metal Laser Sintering Process |
| Author(s) |
Ebrahim Asadi, Behzad Fotovvati, Faridreza Attarzadeh |
| On-Site Speaker (Planned) |
Ebrahim Asadi |
| Abstract Scope |
Direct metal laser sintering (DMLS) is a widely used powder bed fusion additive manufacturing technology that offers extensive capabilities to fabricate complex metallic components. However, this process has several variables (processing parameters), altering which increases the complexity of the correlations between them and the desired properties (responses) in order to optimize the responses. In this study, the influence of the most influential DMLS processing parameters, e.g., laser power, scan speed, hatch spacing, on relative density, microhardness, and various line and surface roughness parameters are thoroughly investigated. The significance of processing parameters on each response are analyzed using the Taguchi method. A multi-objective response surface method (RSM) model is developed for the optimization of DMLS processing parameters considering all the responses. Furthermore, an artificial neural network model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM method. |