|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing of Large-scale Metallic Components
||NOW ON-DEMAND ONLY - Process Optimization in Metal Additive Manufacturing Using Image Processing and Statistical Analysis
||Faiyaz Ahsan, Jafar Razmi, Leila Ladani
|On-Site Speaker (Planned)
Additive manufacturing has attracted widespread attention due to its ability to produce parts with complicated design and less waste because of the additive nature of the process. Process optimization to obtain high quality parts is still a concern which is impeding full scale production of materials. This work focus on gaining useful information such as contact angle, porosity, voids, melt pool and keyhole area from experimentally obtained bead geometry produced using laser powder bed fusion (LPBF) additive manufacturing technique. These features are identified and quantified using process learning (ImageJ) that pertains to different process parameters including laser power and scan speed along with their underlying physics. Finally, a full factorial design will be employed that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to observe the output response individually as well as in a collective manner.
||Additive Manufacturing, Process Technology, Characterization