Thermodynamic Behavior and Defects of Laser Selective Melting Ti6Al4V at Mesoscopic Scale: Numerical Simulation and Experimental Verification
ZHAO Jinmeng1,2,3, LU Lin1,2, WANG Jingrong3, ZHANG Liang1,2,WU Wenheng1,2, ZHU Dong1,2,3, GUO Shuaidong1,2,3, XIAO Congyue1,2
1 Shanghai Engineer Research Center of 3D Printing Materials, Shanghai 200437, China 2 Shanghai Research Institute of Materials, Shanghai 200437, China 3 College of Engineering,Shaghai Polytechnic University,Shanghai 201209, China
Abstract: Based on the three-dimensional Ti6Al4V powder bed model for selective laser melting at mesoscopic scale, the influence of selective laser melting parameters on the internal metallurgical defects of formed parts was studied. With the laser power is increased from 100 W to 350 W, the width of molten pool increases from 62.6 μm to 116.2 μm,the depth increases from 24.9 μm to 32.4 μm. When the laser energy is not enough to penetrate the powder layer, unmelted particles and spheroidization exist in the molten pool. When the laser power is too high, the metal powder will evaporate under sufficient energy, resulting in recoil pressure, which improves splashing ability of the melt and decreases the surface quality of molten pool. According to the simulation process, the forming parts were prepared and tested, the experimental results of porosity and molten pool width are consistent with the simulation results, which indicates that the discontinuous interface model of powder bed based on mesoscopic scale could be used to guide the selection of process parameters and control metallurgical defects in selective laser melting.
1 Wang S Q, Ma T J, Li W Y, et al. Science & Technology of Welding & Joining, 2016, 22(3), 177. 2 闫雪, 阮雪茜. 航空制造技术, 2016, 59(21), 70. 3 Cain V, Thijs L, Van Humbeeck J, et al. Additive Manufacturing, 2015, 5, 68. 4 Zhao X,Li S,Zhang M,et al. Materials & Design, 2016, 95(4), 21. 5 李勇. GH3536合金选区激光熔化成形行为及高温性能研究. 硕士学位论文, 机械科学研究总院, 2019. 6 陶攀, 李怀学, 许庆彦, 巩水利.铸造, 2017, 66(7), 695. 7 Roberts L ,Wang C J, Esterlein R,et al. International Journal of Machine Tools and Manufacture, 2009, 49(12-13), 916. 8 Li Y, Gu D D. Additive Manufacturing, 2014, 1(4), 99. 9 Gu D D, MaC L, Xia M J, et al.Engineering, 2017, 3(5), 675. 10 Dai K, Shaw L. Acta Materialia, 2005, 53(18), 4743. 11 Panwisawas C, Qiu C, Anderson M J, et al. Computational Materials Science, 2017, 126, 479. 12 Hirt C W, Nichols B D. Journal of Computational Physics, 1981, 39(1), 201. 13 向羽, 张树哲, 李俊峰, 等. 浙江大学学报(工学版), 2019, 53(11), 2102.
渝公网安备50019002502923号 版权所有：《材料导报》编辑部
2021, Vol. 35 
