Predicting the porosity defects in selective laser melting (SLM) by molten pool geometry. (15th August 2022)

• Record Type: Journal Article
• Authors: Liu, Binqi; Fang, Gang; Lei, Liping; Yan, Xingchen
• Is Part Of: International journal of mechanical sciences. Volume 228(2022)
• Publication Date: 2022-08-15
• Subjects: Selective laser melting -- Porosity -- Simulation -- Analytical model -- Processing window; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2022.107478
• Languages: English
• ISSNs: 0020-7403

Abstract

Highlights: An analytical methodology to rapidly calculate molten pool dimensions is proposed. Molten pool size evolution during multi-track process is studied and evaluated. Lack of fusion and keyhole pore defects criterion established via simulation study. Predicted SLM process window agrees well with cube samples of different materials. Abstract: A methodology to predict the porosity in selective laser melting (SLM) is put forward. A combination of experiments, numerical, and analytical calculations for predicting the proper SLM processing window of nearly fully dense parts is carried out. The molten pool dimensions, calculated by the analytical models, are used to assess the lack of fusion pores and keyhole pores of the SLM fabricated samples. Better than the previous process window predicting method, the accumulated heat of previous tracks is considered in the model, with the influence of the scanning strategies taken into account. Defect criterion of lack of fusion pores and keyhole pores are proposed, derived from the flow characteristic of the molten pool simulated with a meso-scale CFD numerical model. The calculated results of the molten pool dimensions are consistent with the conducted SLM experiments of SS 316L and Ti-6Al-4V, respectively. The predicted processing windows also match the porosities of corresponding fabricated cube samples. It demonstrates the present methodology will aid the process design and defect elimination during SLM as an effective tool potentially. Graphical abstract: Image, graphical abstract