Experimental and numerical study for direct powder bed selective laser processing (sintering/melting) of silicon carbide ceramic


Monton A., Abdelmoula M., KÜÇÜKTÜRK G. , Maury F., Grossin D., Ferrato M.

MATERIALS RESEARCH EXPRESS, cilt.8, sa.4, 2021 (SCI İndekslerine Giren Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8 Konu: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1088/2053-1591/abf6fc
  • Dergi Adı: MATERIALS RESEARCH EXPRESS

Özet

The study was carried out to investigate the manufacturing possibility of Silicon Carbide (SiC) by direct Powder Bed Selective Laser Processing (PBSLP) experimentally and numerically. The experimental study was carried out by means of PBSLP while the numerical study was accomplished by developing a CFD model. The CFD model simulates accurately realistic conditions of the PBSLP process. A user-defined code, that describes the process parameters such as laser power, scanning speed, scanning strategies, and hatching distance has been developed and compiled to ANSYS FLUENT 2020 R1. Also, the model was validated with the available published data from the literature. The model was used to deeply analyse and support the results obtained through the experimental runs. Different values of laser power and scanning speeds with scanning strategy in the form of a continuous linear pattern and rotated by 90 degrees between layers were studied. The laser power is ranging from 52W to 235 W while the scanning speed is ranging from 300 to 3900 mm s(-1). The results showed that the direct PBSLP of SiC is possible with the optimization of the process parameters. Layer thickness and hatching distance are the most important parameters that needed to be optimized. Also, the laser power and scanning speed needed to be adjusted so that the scanning temperature was between the sintering and the decomposition limits. The good agreement between experimental and simulation results proved the power and ability of the developed CFD model to be a useful tool to analyse and optimize future experimental data.