数字技术在粉末高温合金中的应用与展望

Application and prospect of digital technology in powder metallurgy superalloys

摘要

粉末高温合金因无偏析、组织均匀、性能优异等特点，成为先进航空发动机涡轮盘的首选材料。但是，其高合金化成分和复杂工艺使传统“试错法”研发模式面临周期长、成本高、方向不明确等挑战，数字技术已成为解决上述瓶颈的关键路径。本文概述“电子-原子-介观-宏观”全尺度数字技术体系在粉末高温合金研究领域的应用，并展望其未来发展方向。电子尺度的第一性原理等方法主要用于筛选合金元素、解释相稳定性机制、计算 γ/γ′两相界面能等。原子尺度的分子动力学等方法主要用于揭示夹杂物对裂纹的影响、变形过程中位错、孪晶、晶界等的相互作用机理。介观尺度的晶体塑性、相场、元胞自动机等方法模拟颗粒烧结、两相组织、晶粒等演变过程及其对力学性能的影响。宏观尺度方法侧重制粉、热等静压、热处理等工艺过程宏观应力场、温度场、平均介观组织场（如平均晶粒尺寸）等的演变过程模拟。人工智能与数字孪生正变革粉末高温合金研发：人工智能基于大量实验数据，通过机器学习等不同算法提供成分与工艺优化的明确方向和方案；数字孪生构建虚拟映射，力图实现从制造到服役的全流程精准仿真、实时诊断与寿命预测。最后，指出未来数字技术需向跨尺度跨工序制备——服役一体化预测与优化方向发展，支撑航空发动机涡轮盘的短周期、低成本、高可靠和长寿命升级与创新。

Abstract

Powder metallurgy superalloys have become the preferred material for turbine disks of advanced aero-engines due to their advantages such as segregation-free microstructure， uniform microstructure distribution， and excellent comprehensive properties. However，their highly alloyed composition and complex manufacturing processes make the traditional trial-and-error research mode confronted with multiple challenges， including long development cycles， high research costs， and unclear research orientation. Digital technologies have emerged as a critical approach to breaking through the above bottlenecks. This paper summarizes the applications of the full-scale digital technology system covering electron， atomic， mesoscopic， and macroscopic scales in the research of powder metallurgy superalloys， and prospects its future development trends. At the electronic scale， first-principles calculations and related methods are mainly adopted to screen alloying elements， clarify the mechanism of phase stability， and calculate the interfacial energy of γ/γ′ two-phase structures. At the atomic scale，molecular dynamics simulations are used to reveal the influence of inclusions on crack initiation and propagation， as well as the interaction mechanisms of dislocations， twins， grain boundaries，and other microstructural features during deformation. Mesoscopic scale methods，such as crystal plasticity，phase field， and cellular automaton，are applied to simulate the evolution of particle sintering，two-phase microstructure and grain structure，and their effects on mechanical properties. Macroscopic scale methods focus on simulating the evolution of macroscopic stress field， temperature field and average mesoscopic microstructure field in manufacturing procedures including powder preparation， hot isostatic pressing and heat treatment. Artificial intelligence and digital twin technology are revolutionizing the research and development of powder metallurgy superalloys. Based on massive experimental data，artificial intelligence adopts various algorithms represented by machine learning to provide definite guidance and schemes for composition optimization and process improvement. Digital twin establishes virtual mapping of physical components， aiming to realize accurate full-process simulation， real-time condition diagnosis，and life prediction throughout manufacturing and service stages. Finally，this study points out that future digital technologies need to develop toward the integrated prediction and optimization of cross-scale and cross-process preparation-service performance，so as to support the upgrading and innovation of aero-engine turbine disks with short development cycles，low costs， high reliability，and long service life.

关键词

数字技术 / 粉末高温合金 / 跨尺度模拟 / 人工智能

Key words

digital technology / powder metallurgy superalloy / multi-scale simulation / artificial intelligence

参考文献

[1] 郑亮, 张国庆, 张利冲, 等. 高温合金制备工艺精确定制:策略、方法与验证[J]. 航空材料学报, 2025, 45(5): 1-25.

[2] ZHENG L, ZHANG G Q, ZHANG L C, et al. Precise tai-loring of manufacturing processes for superalloys: strat-egy,methodology and validation[J]. Journal of Aeronau-tical Materials, 2025, 45(5):1-25.

[3] 张义文, 刘建涛. 粉末高温合金研究进展[J]. 中国材料进展, 2013, 32(1):1-11.

[4] ZHANG Y W, LIU J T. Development in powder metal-lurgy superalloy[J]. Materials China, 2013, 32(1):1-11.

[5] 刘孝山, 宋旼, 夏圣旭, 等. 数值模拟技术在高温合金中的应用[J]. 铸造, 2025, 74(7):880-894.

[6] LIU X S, SONG M, XIA S X, et al. Numerical simula-tion technology applications in superalloys[J]. Foundry, 2025, 74(7):880-894.

[7] 姚凯俊, 姚志浩, 王强, 等. 高温合金涡轮盘制备数值模拟方法研究进展[J]. 稀有金属材料与工程, 2022, 51(11):4347-4357.

[8] YAO K J, YAO Z H, WANG Q, et al. Research progress of numerical simulation methods for manufacture tech-nologies of superalloy turbine disks[J]. Rare Metal Mate-rials and Engineering, 2022, 51(11):4347-4357.

[9] HONG X, QIN Z, JIANG X, et al. Compositional and structural control toward boosting inner-grain prestress and releasing the inter-lattice strain of an FGH99 diffu-sion-bonded superalloy[J]. Materials Horizons, 2026.

[10] JIANG Y L, LIU S S, LU R G, et al. Cellular automata simulation of grain growth of powder metallurgy Ni-based superalloy[J]. Journal of Iron and Steel Research International, 2023, 30(4):838-848.

[11] CHI Z L, REN S, QIAO J B, et al. Failure behaviors and processing maps with failure domains for hot compres-sion of a powder metallurgy Ni-based superalloy[J]. Journal of Materials Research and Technology, 2022, 20:3860-3874.

[12] HAO Z P, LOU Z Z, FAN Y H. Study on phase transfor-mation in cutting Ni-base superalloy based on molecular dynamics method[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2021, 235(11):2065-2086.

[13] IQBAL M T, SAEEDA S, ZAHRA T, et al. Next-genera-tion materials discovery using DFT: functional innova-tion[J]. Scholars Journal of Engineering and Technol-ogy, 2025, 13(7):454-486.

[14] KRESSE G, FURTHMÜLLER J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set[J]. Physical Review B, 1996, 54(16):11169-11186.

[15] ALDER B J, WAINWRIGHT T E.Studies in molecular dynamics. Ⅰ. general method[J]. The Journal of Chemi-cal Physics, 1959, 31(2):459-466.

[16] RAZU-MOVSKII I M. First-principles-aided design of a new Ni-base superalloy:influence of transition metal alloying ele-ments on grain boundary and bulk cohesion[J]. Acta Materialia, 2015, 82:369-377.

[17] LI L H, KIM G, CHEN W, et al. Effect of phosphorus on the phase stability of a high refractory content powder-processed Ni-base superalloy[J]. Metallurgical and Mate-rials Transactions A, 2019, 50(11):5459-5475.

[18] XUE H T, LUO Y Q, TANG F L, et al. Solute segrega-tion induced stabilizing and strengthening effects on Ni Σ3 [110](111) symmetrical tilt grain boundary in nickel-based superalloys[J]. Journal of Materials Research and Technology, 2021, 11:1281-1289.

[19] CHENG M L, LIANG M J, LIAO H H. Investigation on tensile properties and failure mechanism of Ni3Al(001)/ Ni3Ta(001) interface using the first-principles method [J]. Vacuum, 2023, 214:112212.

[20] PATRA P, DEY S, GAYATHRI N, et al. Influence of alloying elements on stacking fault energy in Ni and Ni-based alloy: a first-principles study[J]. Computational and Theoretical Chemistry, 2024, 1240:114815.

[21] YAO Z H, HOU J, CHEN Y, et al. Effect of micron-sized particles on the crack growth behavior of a Ni-based pow-der metallurgy superalloy[J]. Materials Science and Engineering:A, 2022, 860:144242.

[22] CHEN Y, YAO Z H, DONG J X, et al. Molecular dynamics simulation of the γ′ phase deformation behaviour in nickel-based superalloys[J]. Materials Sci-ence and Technology, 2022, 38(17):1439-1450.

[23] BOROVIKOV V V, MENDELEV M I, SMITH T M, et al. Molecular dynamics simulation of twin nucleation and growth in Ni-based superalloys[J]. International Journal of Plasticity, 2023, 166:103645.

[24] BOROVIKOV V V, MENDELEV M I, SMITH T M, et al. Effects of alloying elements on twinning in Ni-based superalloys[C]// International Symposium on Superal-loys. Cham: Springer Nature Switzerland, 2024: 1049-1057.

[25] 江河, 佴启亮, 徐超, 等. 镍基高温合金疲劳裂纹急速扩展敏感温度及成因[J]. 金属学报, 2023, 59(9):1190-1200.

[26] JIANG H, NAI Q L, XU C, et al. Sensitive temperature and reason of rapid fatigue crack propagation in nickel-based superalloy[J]. Acta Metallurgica Sinica, 2023, 59(9):1190-1200.

[27] 章海明, 徐帅, 李倩, 等. 晶体塑性理论及模拟研究进展[J]. 塑性工程学报, 2020, 27(5):12-32.

[28] ZHANG H M, XU S, LI Q, et al. Progress of crystal plas-ticity theory and simulations[J]. Journal of Plasticity Engineering, 2020, 27(5):12-32.

[29] MANONUKUL A, DUNNE F P E, KNOWLES D. Phys-ically-based model for creep in nickel-base superalloy C263 both above and below the gamma solvus[J]. Acta Materialia, 2002, 50(11):2917-2931.

[30] VORSTER W J J, DUNNE F P E. Crystal plasticity and multiscale modelling of superalloy creep[J]. Philosophi-cal Magazine, 2012, 92(7):830-848.

[31] PENG Z C, TIAN G F, JIANG J, et al. Mechanistic behaviour and modelling of creep in powder metallurgy FGH96 nickel superalloy[J]. Materials Science and Engi-neering:A, 2016, 676:441-449.

[32] ZHANG T T, COLLINS D M, DUNNE F P E, et al. Crystal plasticity and high-resolution electron backscatter diffraction analysis of full-field polycrystal Ni superalloy strains and rotations under thermal loading[J]. Acta Materialia, 2014, 80:25-38.

[33] CHEN B, JIANG J, DUNNE F P E. Is stored energy den-sity the primary meso-scale mechanistic driver for fatigue crack nucleation?[J]. International Journal of Plasticity, 2018, 101:213-229.

[34] SUN S Y, LI L, YUE Z F, et al. Experimental and numer-ical investigation on fretting fatigue behavior of nickel-based single crystal superalloy at high temperature[J]. Mechanics of Materials, 2020, 150:103595.

[35] ZHENG Z B, PENG Z C. Crystal plasticity simulation of the microstructural effect in powder metallurgy superal-loys under dwell fatigue loading[J]. Crystals, 2022, 12(2):269.

[36] MAO J X, XU Y F, HU D Y, et al. Microstructurally short crack growth simulation combining crystal plastic-ity with extended finite element method[J]. Engineering Fracture Mechanics, 2022, 275:108786.

[37] 刘佳, 高建宝, 李志诚, 等. 金属增材制造的相场模拟现状及展望[J]. 铸造技术, 2023, 44(2):87-100.

[38] LIU J, GAO J B, LI Z C, et al. Recent progress and per-spectives of phase-field modelling in metal additive man-ufacturing[J]. Foundry Technology, 2023, 44(2) : 87-100.

[39] 陈成, 陈铮, 杨涛, 等. 晶体相场模型的研究进展[J]. 材料导报, 2012, 26(9):116-119.

[40] CHEN C, CHEN Z, YANG T, et al. Recent develop-ments and future directions of phase field crystal model[J]. Materials Review, 2012, 26(9):116-119.

[41] FROMM B S, CHANG K, MCDOWELL D L, et al. Linking phase-field and finite-element modeling for pro-cess-structure-property relations of a Ni-base superalloy[J]. Acta Materialia, 2012, 60(17):5984-5999.

[42] WANG X, HUANG Z W, CAI B, et al. Formation mech-anism of abnormally large grains in a polycrystalline nickel-based superalloy during heat treatment process ing[J]. Acta Materialia, 2019, 168:287-298.

[43] FAN X, ZHANG A, GUO Z, et al. Growth behavior of γ′ phase in a powder metallurgy nickel-based superalloy under interrupted cooling process[J]. Journal of Materi-als Science, 2019, 54(3):2680-2689.

[44] KRUTZ N J, SHEN C, FINK C, et al. Experimental cali-bration & multi-scale simulation of multi-modal γ′ precip-itation in nickel superalloys during continuous cool-ing[J]. Metallurgical and Materials Transactions A, 2021, 52(7):3122-3139.

[45] 郭洪民, 刘旭波, 杨湘杰. 元胞自动机方法模拟微观组织演变的建模框架[J]. 材料工程, 2003(8):23-27.

[46] GUO H M, LIU X B, YANG X J. Model framework for microstructure evolution modeling with cellular auto mata[J]. Journal of Materials Engineering, 2003(8) : 23-27.

[47] 吕胡缘, 胡励, 时来鑫, 等. 基于元胞自动机的金属静态再结晶行为研究进展[J]. 材料热处理学报, 2021, 42(2):1-10.

[48] LV H Y, HU L, SHI L X, et al. Research progress of static recrystallization behavior of metals based on cellu-lar automata[J]. Transactions of Materials and Heat Treatment, 2021, 42(2):1-10.

[49] 李继红, 徐蔼彦, 汪强, 等. 基于元胞自动机法模拟凝固过程微观组织的研究进展[J]. 热加工工艺, 2014, 43(15):16-19.

[50] LI J H, XU A Y, WANG Q, et al. Progress in simulation of solidification microstructure based on cellular automata method[J]. Hot Working Technology, 2014, 43(15):16-19.

[51] YANG Y H, ZHANG B Y, CHEN X Q, et al. Modeling and simulation of grain growth for FGH96 superalloy using a developed cellular automaton model[J]. Mod-elling and Simulation in Materials Science and Engineer-ing, 2024, 32(5):055011.

[52] ALSHANBARI H M USMAN, KHIDHIR D M, et al. FEM-based numerical study for enhanced electrical and thermal effectiveness in photovoltaic thermal systems with attached trapezoidal flow channels[J]. Case Studies in Thermal Engineering, 2025, 69:105997.

[53] 郭快快, 陈进, 刘常升, 等. 数值模拟喷射夹角对VIGA制粉雾化过程的影响[J]. 东北大学学报(自然科学版), 2020, 41(5):729-735.

[54] GUO K K, CHEN J, LIU C S, et al. Numerical simula-tion of the intersection angle influence on atomization process of powders produced by VIGA[J]. Journal of Northeastern University(Natural Science), 2020, 41(5): 729-735.

[55] 郭快快, 商硕, 陈进, 等. 数值模拟雾化气压对GH4169合金粉末粒径的影响[J]. 东北大学学报(自然科学版), 2020, 41(6):807-811.

[56] GUO K K, SHANG S, CHEN J, et al. Numerical simula-tion of influence of atomization pressure on particle size of GH4169 alloy powders[J]. Journal of Northeastern University(Natural Science), 2020, 41(6):807-811.

[57] 侯维强, 吴佳欣, 孟杰, 等. 不同雾化压力下 GH3536 合金粉末制备和气雾化过程模拟[J]. 粉末冶金技术, 2023, 41(5):410-419.

[58] HOU W Q, WU J X, MENG J, et al. Atomization simula-tion and preparation of GH3536 powders at different atomization pressures[J]. Powder Metallurgy Technol-ogy, 2023, 41(5):410-419.

[59] LI X G, ZHU Q, SHU S, et al. Fine spherical powder production during gas atomization of pressurized melts through melt nozzles with a small inner diameter[J]. Powder Technology, 2019, 356:759-768.

[60] ZHANG L C, XU W Y, LI Z, et al. Mechanism of rapidly solidified satellites formation in gas atomized powders: simulation and characterization[J]. Powder Technology, 2023, 418:118162.

[61] LUO S, OUYANG Y, LAI S Y, et al. 3D numerical mod-eling of gas atomization process for powder preparation based on similarity theory[J]. Powder Technology, 2024, 433:119244.

[62] 王超, 肖磊, 张高翔, 等. 应用于粉末冶金的修正Drucker-Prager/Cap模型[J]. 稀有金属材料与工程, 2020, 49(9):3115-3121.

[63] WANG C, XIAO L, ZHANG G X, et al. A modified Drucker-Prager/Cap model for powder metallurgy[J]. Rare Metal Materials and Engineering, 2020, 49(9) : 3115-3121.

[64] WANG C, XIA S X, GUO J Z, et al. An enhanced DPC model for metal powder incorporating yield strength of solid-state[J]. Powder Technology, 2024, 435:119409.

[65] PATIL S, MAZOR A, ALMIRALL N, et al. Modeling of powder metallurgy hot isostatic pressing and application to a Ni-base superalloy[C]// International Symposium on Superalloys. Cham: Springer Nature Switzerland, 2024: 1091-1100.

[66] KHOEI A R, AZAMI A R, AZIZI S. Computational modeling of 3D powder compaction processes[J]. Jour-nal of Materials Processing Technology, 2007, 185

[67] ( KHOEI A R, AZAMI A R, AZIZI S. Computational modeling of 3D powder compaction processes[J]. Jour-nal of Materials Processing Technology, 2007, 185(1/2/3):166-172.) :166-172.

[68] KHOEI A R, SHAMLOO A, AZAMI A R. Extended finite element method in plasticity forming of powder compaction with contact friction[J]. International Jour-nal of Solids and Structures, 2006, 43(18/19) : 5421-5448.

[69] 王冰, 李启军, 黄国超, 等. 粉末高温合金热等静压数值模拟研究[J]. 智能制造, 2023( 增刊 1):18-22.

[70] WANG B, LI Q J, HUANG G C, et al. Numerical simu-lation study on hot isostatic pressing of powder metal-lurgy superalloys[J]. Intelligent Manufacturing, 2023(Suppl 1):18-22.

[71] 瞿宗宏, 王泽钰, 宋嘉明, 等. 热等静压态 FGH4097 合金的热变形行为及组织演变规律[J]. 材料热处理学报, 2025, 46(6):78-89.

[72] QU Z H, WANG Z Y, SONG J M, et al. Hot deforma-tion behavior and microstructure evolution of hot iso-static pressed FGH4097 superalloy[J]. Transactions of Materials and Heat Treatment, 2025, 46(6):78-89.

[73] VAN NGUYEN C, BEZOLD A, BROECKMANN C. Inclusion of initial powder distribution in FEM mod-elling of near net shape PM hot isostatic pressed compo-nents[J]. Powder Metallurgy, 2014, 57(4):295-303.

[74] 于传伟. 铝和钛合金粉末振动堆积致密化的物理实验和数值模拟研究[D]. 沈阳: 东北大学, 2020.

[75] YU C W. Physical experiments and DEM numerical sim-ulation on the vibrated packing densification of alu-minum and titanium alloy powders[D]. Shenyang: Northeastern University, 2020.

[76] ABENA A, ARISTIZABAL M, ESSA K. Comprehen-sive numerical modelling of the hot isostatic pressing of Ti-6Al-4V powder: from filling to consolidation[J]. Advanced Powder Technology, 2019, 30(11) : 2451-2463.

[77] 陈凯歌. 新型热等静压态镍基粉末高温合金高温拉伸变形行为研究[D]. 长沙: 中南大学, 2022.

[78] CHEN K G. Study on the hot tensile deformation behav-iors of a new hot isostatic pressing nickel-based powder metallurgy superalloy[D]. Changsha: Central South Uni-versity, 2022.

[79] 霍万晨, 刘建涛, 张明, 等. 一种新型四代镍基粉末高温合金挤压过程数值模拟及试验验证[J]. 材料热处理学报, 2021, 42(2):152-159.

[80] HUO W C, LIU J T, ZHANG M, et al. Numerical simu-lation and experimental verification of extrusion process of a new fourth-generation nickel-based powder metal-lurgy superalloy[J]. Transactions of Materials and Heat Treatment, 2021, 42(2):152-159.

[81] 温红宁, 金俊松, 滕庆, 等. 新型镍基粉末高温合金包覆挤压数值模拟与工艺窗口优化[J]. 中国有色金属学报, 2022, 32(9):2664-2679.

[82] WEN H N, JIN J S, TENG Q, et al. Numerical simula-tion and processing window optimization on cladding extrusion of novel nickel-based powder superalloy[J]. The Chinese Journal of Nonferrous Metals, 2022, 32(9): 2664-2679.

[83] 杨秋梅. 热等静压态 FGH4113A 合金热变形行为及热挤压工艺优化[D]. 长沙: 中南大学, 2023.

[84] YANG Q M. Hot deformation behavior and hot extrusion process optimization of the HIPed FGH4113A superal-loy[D]. Changsha: Central South University, 2023.

[85] DYE D, CONLON K T, REED R C. Characterization and modeling of quenching-induced residual stresses in the nickel-based superalloy IN718[J]. Metallurgical and Materials Transactions A, 2004, 35(6):1703-1713.

[86] ZHANG Z W, FENG Y F, TAN Q, et al. Residual stress distribution in Ni-based superalloy turbine discs during fabrication evaluated by neutron/X-ray diffraction mea-surement and thermomechanical simulation[J]. Materi-als & Design, 2019, 166:107603.

[87] FAN D G, ZHANG J, FENG Y F, et al. Residual stress and precipitation evolution of bulk nickel-based superal-loy during heat treatment[J]. Metallurgical and Materials Transactions A, 2025, 56(1):125-139.

[88] LIU Z F, CHENG J Y, WANG C, et al. Quantitative microstructure prediction of powder high-temperature alloy during solution heat treatment and its validation[J]. Metals, 2024, 14(7):769.

[89] 程俊义, 刘朝峰, 马向东, 等. 一种新型镍基粉末高温合金涡轮盘的双性能热处理工艺研究[J]. 稀有金属材料与工程, 2024, 53(9):2614-2623.

[90] CHENG J Y, LIU Z F, MA X D, et al. Dual-microstruc-ture heat treatment process for novel nickel-based pow-der metallurgy superalloy turbine disk[J]. Rare Metal Materials and Engineering, 2024, 53(9):2614-2623.

[91] LIU Z F, WANG C, CHENG J Y, et al. An improved grain growth model and its application in gradient heat treatment of aero-engine turbine discs[J]. Materials, 2023, 16(19):6584.

[92] 汪煜, 丁方政, 刘玉峰, 等. FGH96合金环件风冷淬火过程数值模拟[J]. 特种铸造及有色合金, 2025, 45(10):1529-1535.

[93] WANG Y, DING F Z, LIU Y F, et al. Numerical simula-tion of air-cooling quenching of FGH96 ring[J]. Special Casting & Nonferrous Alloys, 2025, 45(10):1529-1535.

[94] 毛涵驰. 镍基合金涡轮盘固溶气淬过程温度及应力场特性研究[D]. 哈尔滨: 哈尔滨工业大学, 2023.

[95] MAO H C. Research on temperature and stress field char-acteristics of nickel based alloy turbine disk during solu-

[96] tion gas quenching process[D]. Harbin: Harbin Institute of Technology, 2023.

[97] 高胜东, 于鑫. 盘坯温度分布参数系统冷速控制并行联合仿真[J]. 系统仿真学报, 2021, 33(8):1839-1845.

[98] GAO S D, YU X. Co-simulation of parallel computing of disc temperature distributed parameter system cooling rate control[J]. Journal of System Simulation, 2021, 33(8):1839-1845.

[99] MENOU E, RAMSTEIN G, BERTRAND E, et al. Multi-objective constrained design of nickel-base superalloys using data mining-and thermodynamics-driven genetic algorithms[J]. Modelling and Simulation in Materials Science and Engineering, 2016, 24(5):055001.

[100] CONDUIT B D, JONES N G, STONE H J, et al. Design of a nickel-base superalloy using a neural network[J]. Materials & Design, 2017, 131:358-365.

[101] XU B, YIN H Q, JIANG X, et al. Data-driven design of Ni-based turbine disc superalloys to improve yield strength[J]. Journal of Materials Science & Technology, 2023, 155:175-191.

[102] ZHAO W Y, REN Q G, YAO Z H, et al. Multi-scale cal-culation-aided composition optimization design of low-density and high-strength nickel-based superalloy[J]. Metallurgical and Materials Transactions A, 2023, 54(10):3796-3819.

[103] MA Q S, LI X T, XIN R F, et al. Thermodynamic calcu-lation and machine learning aided composition design of new nickel-based superalloys[J]. Journal of Materials Research and Technology, 2023, 26:4168-4178.

[104] 祝亚亮, 雍维, 杨杰, 等. 热力学计算与机器学习相结合逆向设计镍基高温合金[J]. 材料工程, 2024, 52(6):167-176.

[105] ZHU Y L, YONG W, YANG J, et al. Reverse design of nickel-based superalloys based on thermodynamic calcu-lation and machine learning[J]. Journal of Materials Engineering, 2024, 52(6):167-176.

[106] WU T, WANG W L, YE X H, et al. Transient micro-liq-uid phase HIP sintering of powder metallurgy superal-loys achieved by a binary composition-splitting strategy[J]. Journal of Materials Research and Technol-ogy, 2026, 40:1528-1546.

[107] WEN H N, JIN J S, TANG X F, et al. Machine learning-assisted constitutive modeling of a novel powder metal-lurgy superalloy[J]. International Journal of Mechanical Sciences, 2023, 260:108654.

[108] WEN H N, WANG S L, JIN J S, et al. Deep learning-based modeling of the strain rate-dependent thermome-chanical processing response for a novel HIPed P/M nickel-based superalloy[J]. Journal of Materials Process-ing Technology, 2024, 324:118226.

[109] XIONG J, HE J C, LENG X S, et al. Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy[J]. Journal of Materials Science & Technology, 2023, 146:177-185.

[110] ZHANG M, LIU G Q, WANG H, et al. Modeling of ther-mal deformation behavior near γ′ solvus in a Ni-based powder metallurgy superalloy[J]. Computational Materi-als Science, 2019, 156:241-245.

[111] LI Y Q, ZHANG Z M, CHENG J Y, et al. The integra-tion of ANN and FEA and its application to property pre-diction of dual-performance turbine disks[J]. Materials, 2024, 17(13):3045.

[112] COHN R, ANDERSON I, PROST T, et al. Instance seg-mentation for direct measurements of satellites in metal powders and automated microstructural characterization from image data[J]. JOM, 2021, 73(7):2159-2172.

[113] CHEN H Y, LIN C C, HORNG M H, et al. Deep learn-ing applied to defect detection in powder spreading pro-cess of magnetic material additive manufacturing[J]. Materials, 2022, 15(16):5662.

[114] THOME P, ARCINIAGA L F, TIN S.“ Microstructure informatics” of polycrystalline Ni-base superalloys using computer vision techniques to understand properties and performance[C]// International Symposium on Superal-loys. Cham: Springer Nature Switzerland,2024:17-32.

[115] CALVAT M, BEAN C, ANJARIA D, et al. Learning metal microstructural heterogeneity through spatial map-ping of diffraction latent space features[J]. npj Computa-tional Materials, 2025, 11:284.

[116] CALVAT M, BEAN C, ANJARIA D, et al. Plasticity encoding and mapping during elementary loading for accelerated mechanical properties prediction[J]. Scripta Materialia, 2026, 273:117082.

[117] CASTILLÓN M, ROMERO I, SEGURADO J. A phase-field approach to fatigue analysis: bridging theory and simulation[J]. International Journal of Fatigue, 2026, 205:109397.

[118] WANG J, HUANG H L, XIN D, et al. Dissolution behavior and kinetics of the γ′ precipitates within a novel powder metallurgy Ni-based superalloy[J]. Journal of Materials Research and Technology, 2024, 30: 1950-1961.

[119] SHEN J, HU X A, MI D, et al. Thermomechanical fatigue behavior and lifetime modeling of powder metal-lurgy superalloy considering phase angle effect[J]. Inter-national Journal of Fatigue, 2022, 164:107164.

[120] XIE R K, ZHONG X C, QIN S H, et al. Predicting multi-axial fatigue life of FGH96 superalloy based on machine learning models by considering failure process and load-ing paths[J]. International Journal of Fatigue, 2023, 175: 107730.

[121] 金衍成, 高建宝, 陈诗瑶, 等. 镍基粉末高温合金的室温和高温力学性能:机器学习建模与实验验证[J]. 智能安全, 2024, 3(2):55-69.

[122] JIN Y C, GAO J B, CHEN S Y, et al. Room/high-temper-ature mechanical properties of nickel-based powder met-allurgy superalloys: machine learning modeling and experimental validation[J]. Artificial Intelligence Secu-rity, 2024, 3(2):55-69.

[123] 张国栋, 苏宝龙, 廖玮杰, 等. 机器学习在高温合金粉末盘构件疲劳寿命预测中的应用[J]. 铸造技术, 2022, 43(7):519-524.

[124] ZHANG G D, SU B L, LIAO W J, et al. Prediction of fatigue life of powder metallurgy superalloy disk via machine learning[J]. Foundry Technology, 2022, 43(7): 519-524.

[125] CHOI J, LEE B. Accelerating materials language process-ing with large language models[J]. Communications Materials, 2024, 5(1):13.

[126] XIE T, WAN Y, LIU Y, et al.Darwin 1.5: large lan-guage models as materials science adapted learners [PP/OL]. arXiv.(2024-12-16)[2026-02-07]. https://arxiv.org/abs/2412.11970.

[127] 王乐, 周军, 崔艳林. 数字孪生在航空发动机领域的应用分析[J]. 航空动力, 2020(5):63-66.

[128] WANG L, ZHOU J, CUI Y L. Application of digital twin in aero engine[J]. Aerospace Power, 2020(5):63-66.

[129] MIKULA J, LASKOWSKI R, DAI L, et al. Advanced “ digital twin” platform for powder-bed fusion additive manufacturing[J]. Hitachi Metals Technical Review, 2021, 37(10):10-23.

基金资助

西北工业大学凝固技术全国重点实验室开放课题(2025-TS-11)