Relating the surface topography of as-built Inconel 718 surfaces to laser powder bed fusion process parameters using multivariate regression analysis

2021 ◽  
Author(s):  
Sean Detwiler ◽  
Dillon Watring ◽  
Ashley Spear ◽  
Bart Raeymaekers
Keyword(s):  
Regression Analysis ◽  
Surface Topography ◽  
Process Parameters ◽  
Inconel 718 ◽  
Multivariate Regression ◽  
Multivariate Regression Analysis ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals Role of laser powder bed fusion process parameters in crystallographic texture of additive manufactured Nb-48Ti alloy

2021 ◽  
Author(s):  
Rafael de Moura Nobre ◽  
Willy Ank de Morais ◽  
Matheus Tavares Vasques ◽  
Jhoan Guzmán ◽  
Daniel Luiz Rodrigues Junior ◽  
...  
Keyword(s):  
Process Parameters ◽  
Crystallographic Texture ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Tuğrul Özel ◽  
Ayça Altay ◽  
Bilgin Kaftanoğlu ◽  
Richard Leach ◽  
Nicola Senin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Density ◽  
Surface Topography ◽  
Process Parameters ◽  
Surface Texture ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Texture Parameters ◽  
Focus Variation

Abstract The powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.

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