Simulation of the Formation Mechanism of Segregation Bands During IN718 Cladding on 316L Using Laser Powder Bed Fusion
Received date: 2023-10-18
Revised date: 2024-01-22
Online published: 2024-02-04
Supported by
National Natural Science Foundation of China(52035014);Natural Science Foundation of Zhejiang Province(LD22E050013);“Leading Goose” Research and Development Program of Zhejiang Province
During laser cladding, welding, and other hot forming processes, dissimilar metals can form segregation bands. These bands often lead to solidification cracks that can directly affect the mechanical properties of the processed and formed materials. To study the formation mechanism and evolution of segregation bands during metallurgical bonding of dissimilar metal materials, a two-dimensional melting and solidification model for laser cladding of IN718 on 316L stainless steel was used. This model was established using the cellular automata method and Eulerian multiphase flow algorithm. The evolution of the temperature field, molten pool morphology, melt flow, and element distributions during laser cladding was comprehensively analyzed. The rationality of the model was confirmed by comparing the melt pool geometry and grain orientation. Additionally, the reliability of the model was confirmed by comparing the distribution of Fe element content in the x- and y-directions. The simulation results reveal that during the metallurgical bonding process of laser cladding IN718 alloy on the 316L stainless steel substrate, distinct segregation zones are observed, which are characterized by the enrichment of Fe and Ni elements and an unideal alternating distribution pattern. This finding is in high consistency with the experimental results. The Marangoni force drives more Fe elements from the bottom of the melt pool (substrate) to the rear end of the melt pool, increasing the temperature of the liquidus at that location. This solidification promotion at the rear end of the melt pool causes actual solidification liquidus temperature(Ta) to be biased toward substrate liquidus temperature(Tb), resulting in the formation of a region with a high concentration of Fe elements. The rear end of the melt pool takes on a “bulging” shape, increasing the melt flow rate within the pool. This increase rolls more Fe elements from the front end of the melt pool (powder) to the rear end of the pool. Consequently, the liquidus temperature at the rear end of the melt pool decreases, biasing Ta toward the liquidus temperature of the powder(Tp). This process hinders solidification at the rear end of the melt pool, resulting in the formation of a region with a reduced concentration of Fe elements. The rear end of the melt pool flattens gradually, decreasing the melt flow rate and drawing more elements from the front to the rear end. This change results in the formation of a segregation zone with an alternating distribution of high and low Fe element content, which is consistent with the experimental results. Through the analysis of the distribution of Fe elements in the melt pool, the morphology of the melt pool and the evolution of the melt flow state, it is evident that this segregation zone arises from the mismatch between fluid flow dynamics and the morphological changes of the melt pool during the solidification process. The rate at which the solid-liquid interface moves can be calculated by finding the difference in the solute concentration between the interface neighboring cells. Fluctuations in the molten pool flow cause the morphology of the rear end of the molten pool to constantly change, resulting in varying concentrations of Fe element after solidification. Therefore, increasing the homogeneity of element mixing in the molten pool can reduce the degree of segregation. During the experimental process, appropriate increase in the laser power and scanning rate reduction can improve the quality of the cladding layer.
Key words: segregation zone; laser cladding; cellular automata; element distribution
SHEN Mengkai , DONG Taining , GE Honghao , SHI Xinsheng , ZHANG Qunli , LIU Yunfeng , YAO Jianhua . Simulation of the Formation Mechanism of Segregation Bands During IN718 Cladding on 316L Using Laser Powder Bed Fusion[J]. Acta Metall Sin, 2025 , 61(8) : 1193 -1202 . DOI: 10.11900/0412.1961.2023.00422
| [1] | Chen N N, Khan H A, Wan Z X, et al. Microstructural characteristics and crack formation in additively manufactured bimetal material of 316L stainless steel and Inconel 625 [J]. Addit. Manuf., 2020, 32: 101037 |
| [2] | Barr C, Sun S D, Easton M, et al. Influence of macrosegregation on solidification cracking in laser clad ultra-high strength steels [J]. Surf. Coat. Technol., 2018, 340: 126 |
| [3] | Soysal T, Kou S, Tat D, et al. Macrosegregation in dissimilar-metal fusion welding [J]. Acta Mater., 2016, 110: 149 |
| [4] | Liu J, Li J, Cheng X, et al. Effect of dilution and macrosegregation on corrosion resistance of laser clad Aermet100 steel coating on 300M steel substrate [J]. Surf. Coat. Technol., 2017, 325: 352 |
| [5] | Gan Z T, Liu H, Li S X, et al. Modeling of thermal behavior and mass transport in multi-layer laser additive manufacturing of Ni-based alloy on cast iron [J]. Int. J. Heat Mass Transf., 2017, 111: 709 |
| [6] | Ge H H, Xu H Z, Wang J F, et al. Investigation on composition distribution of dissimilar laser cladding process using a three-phase model [J]. Int. J. Heat Mass Transf., 2021, 170: 120975 |
| [7] | Li Z Y, Yu G, He X L, et al. Fluid flow and solute dilution in laser linear butt joining of 304SS and Ni [J]. Int. J. Heat Mass Transf., 2020, 161: 120233 |
| [8] | Wolff S J, Gan Z T, Lin S, et al. Experimentally validated predictions of thermal history and microhardness in laser-deposited Inconel 718 on carbon steel [J]. Addit. Manuf., 2019, 27: 540 |
| [9] | Ren N, Li J, Panwisawas C, et al. Thermal-solutal-fluid flow of channel segregation during directional solidification of single-crystal nickel-based superalloys [J]. Acta Mater., 2021, 206: 116620 |
| [10] | Chen R, Xu Q Y, Liu B C. A modified cellular automaton model for the quantitative prediction of equiaxed and columnar dendritic growth [J]. J. Mater. Sci. Technol., 2014, 30: 1311 |
| [11] | Wang W L, Ji C, Luo S, et al. Modeling of dendritic evolution of continuously cast steel billet with cellular automaton [J]. Metall. Mater. Trans., 2018, 49B: 200 |
| [12] | Nastac L. Numerical modeling of solidification morphologies and segregation patterns in cast dendritic alloys [J]. Acta Mater., 1999, 47: 4253 |
| [13] | Beltran-Sanchez L, Stefanescu D M. A quantitative dendrite growth model and analysis of stability concepts [J]. Metall. Mater. Trans., 2004, 35A: 2471 |
| [14] | Zhu M F, Stefanescu D M. Virtual front tracking model for the quantitative modeling of dendritic growth in solidification of alloys [J]. Acta Mater., 2007, 55: 1741 |
| [15] | Zhang H, Xu Q Y, Shi Z X, et al. Numerical simulation of dendrite grain growth of DD6 superalloy during directional solidification process [J]. Acta Metall. Sin., 2014, 50: 345 |
| 张 航, 许庆彦, 史振学 等. DD6高温合金定向凝固枝晶生长的数值模拟研究 [J]. 金属学报, 2014, 50: 345 | |
| [16] | Wang W L, Wang Z H, Yin S W, et al. Numerical simulation of solute undercooling influenced columnar to equiaxed transition of Fe-C alloy with cellular automaton [J]. Comput. Mater. Sci., 2019, 167: 52 |
| [17] | Chen R, Xu Q Y, Liu B C. Cellular automaton simulation of three-dimensional dendrite growth in Al-7Si-Mg ternary aluminum alloys [J]. Comput. Mater. Sci., 2015, 105: 90 |
| [18] | Li J, Wu M, Hao J, et al. Simulation of channel segregation using a two-phase columnar solidification model—part i: Model description and verification [J]. Comput. Mater. Sci., 2012, 55: 407 |
| [19] | Xu H Z, Ge H H, Wang J F, et al. Effects of process parameters upon chromium element distribution in laser-cladded 316L stainless steel [J]. Chin. J. Lasers, 2020, 47(12): 1202004 |
| 徐瀚宗, 葛鸿浩, 王杰锋 等. 工艺参数对316L不锈钢激光熔覆层中Cr元素分布的影响 [J]. 中国激光, 2020, 47(12): 1202004 | |
| [20] | Ren F L, Ge H H, Fang H, et al. Simulation of the dendrite growth during directional solidification under steady magnetic field using three-dimensional cellular automaton method coupled with eulerian multiphase [J]. Int. J. Heat Mass Transf., 2024, 218: 124809 |
| [21] | Luo S, Zhu M Y. A two-dimensional model for the quantitative simulation of the dendritic growth with cellular automaton method [J]. Comput. Mater. Sci., 2013, 71: 10 |
| [22] | Zhu M F, Tang Q Y, Zhang Q Y, et al. Cellular automaton modeling of microstructure evolution during alloy solidification [J]. Acta Metall. Sin., 2016, 52: 1297 |
| 朱鸣芳, 汤倩玉, 张庆宇 等. 合金凝固过程中显微组织演化的元胞自动机模拟 [J]. 金属学报, 2016, 52: 1297 | |
| [23] | Tan W D, Wen S Y, Bailey N, et al. Multiscale modeling of transport phenomena and dendritic growth in laser cladding processes [J]. Metall. Mater. Trans., 2011, 42B: 1306 |
| [24] | Leung C L A, Marussi S, Atwood R C, et al. In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing [J]. Nat. Commun., 2018, 9: 1355 |
| [25] | Zhao C, Fezzaa K, Cunningham R W, et al. Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction [J]. Sci. Rep., 2017, 7: 3602 |
| [26] | Bidare P, Bitharas I, Ward R M, et al. Fluid and particle dynamics in laser powder bed fusion [J]. Acta Mater., 2018, 142: 107 |
| [27] | Li X X, Tan W D. Numerical investigation of effects of nucleation mechanisms on grain structure in metal additive manufacturing [J]. Comput. Mater. Sci., 2018, 153: 159 |
| [28] | Wei H L, Knapp G L, Mukherjee T, et al. Three-dimensional grain growth during multi-layer printing of a nickel-based alloy Inconel 718 [J]. Addit. Manuf., 2019, 25: 448 |
| [29] | Aucott L, Dong H B, Mirihanage W, et al. Revealing internal flow behaviour in arc welding and additive manufacturing of metals [J]. Nat. Commun., 2018, 9: 5414 |
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