Selective laser melting of typical metallic materials: An effective process prediction model developed by energy absorption and consumption analysis

Zhou, Y. H.1,2; Zhang, Z. H.1,2; Wang, Y. P.1; Liu, G.1; Zhou, S. Y.1; Li, Y. L.3; Shen, J.4,5; Yan, M.1

2019-01

10.1016/j.addma.2018.10.046

Additive Manufacturing   影响因子和分区

2214-8604

2214-7810

Selective laser melting (SLM) is a laser-based additive manufacturing technique that can fabricate parts with complex geometries and sufficient mechanical properties. However, the optimal SLM process windows of metallic materials are difficult to predict, especially when exploring new metallic materials. In this paper, a universal and simplified model has been proposed to predict the energy density suitable for SLM of a variety of metallic materials including Ti and Ti alloys, Al alloy, Ni-based superalloy and steel, on the basis of the relationship between energy absorption and consumption during SLM. Several important but easily overlooked factors, including the surface structure of metallic powder, porosity of powder bed, vaporization and heat loss, were considered to improve the accuracy of the model. Results show that, to achieve near-full density parts, the energy absorption (Q(a)) by the local powder bed should be approximately 3-8 times greater than the energy consumption (Q(c))and this finding applies to all materials investigated. The value of Q(a)/Q(c) highly depends on material properties, particularly laser absorptivity, latent heat of melting and specific heat capacity. Experiments on high-entropy alloy (CrMnFeCoNi) and Hastelloy X alloy, new metallic materials for SLM, have been further conducted to verify the model. Results confirm that the model can predict suitable laser energy densities needed for processing the various metallic materials without tedious trial and error experiments. Indications and uncertainty of the model have also been analyzed.

Selective laser melting Process prediction Laser energy density Modeling Metallic materials

SCI ; EI

英语

第一 ; 通讯

National Science Foundation of Guangdong Province[2016A030313756]

Engineering ; Materials Science

Engineering, Manufacturing ; Materials Science, Multidisciplinary

WOS:000456378800020

ELSEVIER SCIENCE BV

20191306683249

3D printers ; Aluminum alloys ; Chromium alloys ; Cobalt alloys ; Energy absorption ; Energy utilization ; Forecasting ; Manganese alloys ; Mechanical properties ; Melting ; Metals ; Models ; Nickel alloys ; Nickel steel ; Selective laser melting ; Specific heat ; Surface structure ; Titanium alloys ; Uncertainty analysis

Energy Utilization:525.3 ; Aluminum Alloys:541.2 ; Titanium and Alloys:542.3 ; Chromium and Alloys:543.1 ; Manganese and Alloys:543.2 ; Nickel Alloys:548.2 ; Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth Metals:549.3 ; Thermodynamics:641.1 ; Laser Applications:744.9 ; Printing Equipment:745.1.1 ; Chemical Operations:802.3 ; Probability Theory:922.1 ; Physical Properties of Gases, Liquids and Solids:931.2 ; Materials Science:951

Web of Science

1.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China
2.Harbin Inst Technol, Sch Mat Sci & Engn, Harbin 150001, Heilongjiang, Peoples R China
3.Nanchang Univ, Mech & Elect Engn Sch, Key Lab Robot & Welding Automat Jiangxi Prov, Nanchang 330031, Jiangxi, Peoples R China
4.Shenzhen Univ, Coll Mech & Control Engn, Shenzhen 510000, Peoples R China
5.Tohoku Univ, Inst Mat Res, Sendai, Miyagi 9808577, Japan

Zhou, Y. H.,Zhang, Z. H.,Wang, Y. P.,et al. Selective laser melting of typical metallic materials: An effective process prediction model developed by energy absorption and consumption analysis[J]. Additive Manufacturing,2019,25:204-217.

Zhou, Y. H..,Zhang, Z. H..,Wang, Y. P..,Liu, G..,Zhou, S. Y..,...&Yan, M..(2019).Selective laser melting of typical metallic materials: An effective process prediction model developed by energy absorption and consumption analysis.Additive Manufacturing,25,204-217.

Zhou, Y. H.,et al."Selective laser melting of typical metallic materials: An effective process prediction model developed by energy absorption and consumption analysis".Additive Manufacturing 25(2019):204-217.