| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Machine-Learning Assisted Prediction of Surface Roughness in Powder Bed Fusion Process with Inconel Super Alloy |
| Author(s) |
Santosh Kumar Rauniyar, Mathew Farias, Ben Xu |
| On-Site Speaker (Planned) |
Santosh Kumar Rauniyar |
| Abstract Scope |
Controlling surface roughness is often a consideration when optimizing the powder bed fusion process for specific applications. Several factors and printing parameters contribute to the surface profiles, with laser power and scan speed being among the most influential. This paper presents an investigation into the prediction capability of machine learning algorithm to estimate the surface roughness profiles. Parts are printed by varying power and scan speed on five different levels using a design of experiments approach. Surface roughness data is acquired on the side surfaces of the as-built parts using a laser confocal microscope. The collected profile data is transformed using multiple feature extraction techniques and is then utilized to train a Neural Network. It is used to classify multiple line profiles labeled according to the parameter variation. The trained neural network demonstrates high accuracy in classifying line profiles to their associated laser parameters when tested with the new data. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |