000150254 001__ 150254
000150254 005__ 20251017144641.0
000150254 0247_ $$2doi$$a10.1088/2053-1591/abf6fc
000150254 0248_ $$2sideral$$a138409
000150254 037__ $$aART-2021-138409
000150254 041__ $$aeng
000150254 100__ $$0(orcid)0000-0003-3948-9520$$aMontón, Alejandro
000150254 245__ $$aExperimental and numerical study for direct powder bed selective laser processing (sintering/melting) of silicon carbide ceramic
000150254 260__ $$c2021
000150254 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150254 5203_ $$aThe study was carried out to investigate the manufacturing possibility of Silicon Carbide (SiC) by direct Powder Bed Selective Laser Processing (PBSLP) experimentally and numerically. The experimental study was carried out by means of PBSLP while the numerical study was accomplished by developing a CFD model. The CFD model simulates accurately realistic conditions of the PBSLP process. A user-defined code, that describes the process parameters such as laser power, scanning speed, scanning strategies, and hatching distance has been developed and compiled to ANSYS FLUENT 2020 R1. Also, the model was validated with the available published data from the literature. The model was used to deeply analyse and support the results obtained through the experimental runs. Different values of laser power and scanning speeds with scanning strategy in the form of a continuous linear pattern and rotated by 90 degrees between layers were studied. The laser power is ranging from 52W to 235 W while the scanning speed is ranging from 300 to 3900 mm s-1. The results showed that the direct PBSLP of SiC is possible with the optimization of the process parameters. Layer thickness and hatching distance are the most important parameters that needed to be optimized. Also, the laser power and scanning speed needed to be adjusted so that the scanning temperature was between the sintering and the decomposition limits. The good agreement between experimental and simulation results proved the power and ability of the developed CFD model to be a useful tool to analyse and optimize future experimental data.
000150254 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000150254 590__ $$a2.025$$b2021
000150254 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b261 / 344 = 0.759$$c2021$$dQ4$$eT3
000150254 592__ $$a0.402$$b2021
000150254 593__ $$aBiomaterials$$c2021$$dQ2
000150254 593__ $$aPolymers and Plastics$$c2021$$dQ2
000150254 593__ $$aMetals and Alloys$$c2021$$dQ2
000150254 593__ $$aElectronic, Optical and Magnetic Materials$$c2021$$dQ2
000150254 594__ $$a3.8$$b2021
000150254 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150254 700__ $$aAbdelmoula, Mohammed
000150254 700__ $$aKüçüktürk, Gökhan
000150254 700__ $$aMaury, Francis
000150254 700__ $$aGrossin, David
000150254 700__ $$aFerrato, Marc
000150254 773__ $$g8, 4 (2021), 045603 [14 pp.]$$pMat. res. express$$tMaterials Research Express$$x2053-1591
000150254 8564_ $$s3653176$$uhttps://zaguan.unizar.es/record/150254/files/texto_completo.pdf$$yVersión publicada
000150254 8564_ $$s2303790$$uhttps://zaguan.unizar.es/record/150254/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150254 909CO $$ooai:zaguan.unizar.es:150254$$particulos$$pdriver
000150254 951__ $$a2025-10-17-14:32:14
000150254 980__ $$aARTICLE