Scanning Strategy Investigation for Direct Powder Bed Selective Laser Processing of Silicon Carbide Ceramic - Archive ouverte HAL
Journal Articles Applied Sciences Year : 2022

Scanning Strategy Investigation for Direct Powder Bed Selective Laser Processing of Silicon Carbide Ceramic

Abstract

Direct-Powder Bed Selective Laser Processing (D-PBSLP) is considered a promising technique for the Additive Manufacturing (AM) of Silicon Carbide (SiC). For the successful D-PBSLP of SiC, it is necessary to understand the effects of process parameters. The process parameters are the laser power, scanning speed, hatching distance, and scanning strategies. This study investigates the effect of scanning strategies on the D-PBSLP of SiC and ensures that other process parameters are appropriately selected to achieve this. A numerical model was developed to obtain the proper process parameters for the investigation of scanning strategies in this work. Different scanning strategies available in the commercial Phoenix 3D printer manufactured by 3D Systems, such as concentric in–out, linear, inclined zigzag, and hexagonal, have been investigated. It was concluded that the zigzag strategy is the best scanning strategy, as it was seen that SiC samples could be printed at a high relative density of above 80% without a characteristic pattern on the layer’s top surface. SiC samples were successfully printed using different laser powers and scanning speeds obtained from the numerical model and zigzag strategy. Additionally, complex geometry in the form of triple periodic minimum surface (gyroid) was also successfully printed.
Fichier principal
Vignette du fichier
Abdelmoula_2022.pdf (93.62 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03930688 , version 1 (30-05-2023)

Identifiers

Cite

Mohamed Abdelmoula, Alejandro Montón Zarazaga, Gökhan Küçüktürk, Francis Maury, David Grossin, et al.. Scanning Strategy Investigation for Direct Powder Bed Selective Laser Processing of Silicon Carbide Ceramic. Applied Sciences, 2022, 12 (2), pp.788. ⟨10.3390/APP12020788⟩. ⟨hal-03930688⟩
43 View
8 Download

Altmetric

Share

More