EI、Scopus 收录
中文核心期刊
Quick Search Issue Search Advanced Search
Volume 56 Issue 7
Jul.  2024
Turn off MathJax
Article Contents
Abstract
References
Related Articles
Hu Yanan, Yu Huan, Wu Shengchuan, Ao Ni, Kan Qianhua, Wu Zhengkai, Kang Guozheng. Machine learned mechanical properties prediction of additively manufactured metallic alloys: progress and challenges. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 1892-1915. DOI: 10.6052/0459-1879-23-542
Citation: Hu Yanan, Yu Huan, Wu Shengchuan, Ao Ni, Kan Qianhua, Wu Zhengkai, Kang Guozheng. Machine learned mechanical properties prediction of additively manufactured metallic alloys: progress and challenges. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 1892-1915. DOI: 10.6052/0459-1879-23-542
shu

MACHINE LEARNED MECHANICAL PROPERTIES PREDICTION OF ADDITIVELY MANUFACTURED METALLIC ALLOYS: PROGRESS AND CHALLENGES

  • *.

    School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu 611756, China

  • †.

    State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, China

  • Received Date: November 14, 2023
  • Accepted Date: January 28, 2024
  • Available Online: January 28, 2024
  • Published Date: January 29, 2024
    Graphical Abstract
    • Abstract
  • Additive manufacturing (AM) is a revolutionary breakthrough in the manufacturing of modern high-end equipment. In order to promote the mass production and reliable applications of AM-processed components, the major determining factors include manufacturing repeatability, quality reliability, and performance predictability. However, the combined effects of anisotropic microstructure, randomly distributed defects, internal residual stresses, and surface roughness pose a challenge for the prediction accuracy and efficiency of mechanical properties through traditional empirical models and limited testing data. Recently, as an inevitable product of the development of big data and artificial intelligence to a certain stage, machine learning (ML) has demonstrated a great potential for modelling the complex nonlinear relationships among high-dimensional physical quantities, which has received continuous attention in the field of predicting the mechanical properties of AM-processed materials. This paper offers a comprehensive review of the research progress in predicting the mechanical properties of AM-processed metals and components using ML methods. First, the common ML algorithms (parametric and non-parametric models) and general ML procedures (data preparation, model establishment, and model application and evaluation) are briefly introduced. Special attention is devoted to exploring the characteristics and construction methods of the advanced physics-informed machine learning (PIML), with specific discussions on the physics-informed model input, construction and output. Furthermore, the reasons for the formation of the four major influencing factors on the mechanical properties of AM-processed materials (anisotropic microstructure, manufacturing defects, residual stresses, and surface roughness), and the current application status of ML in predicting these influencing factors are summarized. This paper focuses on the representative research results of ML and PIML in predicting the tensile and fatigue fracture properties of AM-processed metals. Finally, the limitations of ML in predicting the mechanical properties of AM-processed metals, as well as the hot topics and technological prospects, are pointed out.
    • FullText(HTML)
    • References (113)
  • [1]
    吴圣川, 胡雅楠, 杨冰等. 增材制造材料缺陷表征及结构完整性评定方法研究综述. 机械工程学报, 2021, 57(22): 3-34 (Wu Shengchuan, Hu Yanan, Yang Bing, et al. Review on defect characterization and structural integrity assessment method of additively manufactured materials. Journal of Mechanical Engineering, 2021, 57(22): 3-34 (in Chinese) doi: 10.3901/JME.2021.22.003

    Wu Shengchuan, Hu Yanan, Yang Bing, et al. Review on defect characterization and structural integrity assessment method of additively manufactured materials. Journal of Mechanical Engineering, 2021, 57(22): 3-34 (in Chinese) doi: 10.3901/JME.2021.22.003
    [2]
    卢秉恒. 增材制造技术——现状与未来. 中国机械工程, 2020, 31(1): 19-23 (Lu Bingheng. Additive manufacturing——Current situation and future. China Mechanical Engineering, 2020, 31(1): 19-23 (in Chinese)

    Lu Bingheng. Additive manufacturing——Current situation and future. China Mechanical Engineering, 2020, 31(1): 19-23 (in Chinese)
    [3]
    Tofail SAM, Koumoulos E, Bandyopadhyay A, et al. Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Materials Today, 2018, 21(1): 22-37 doi: 10.1016/j.mattod.2017.07.001
    [4]
    刘伟, 李能, 周标等. 复杂结构与高性能材料增材制造技术进展. 机械工程学报, 2019, 55(20): 128-151, 159 (Liu Wei, Li Neng, Zhou Biao, et al. Progress in additive manufacturing on complex structures and high-performance materials. Journal of Mechanical Engineering, 2019, 55(20): 128-151, 159 (in Chinese)

    Liu Wei, Li Neng, Zhou Biao, et al. Progress in additive manufacturing on complex structures and high-performance materials. Journal of Mechanical Engineering, 2019, 55(20): 128-151, 159 (in Chinese)
    [5]
    Wu ZK, Wu SC, Qian WJ, et al. Structural integrity issues of additively manufactured railway components progress and challenges. Engineering Failure Analysis, 2023, 149: 107265 doi: 10.1016/j.engfailanal.2023.107265
    [6]
    Kim H, Cha M, Kim BC, et al. Maintenance framework for repairing partially damaged parts using 3D printing. International Journal of Precision Engineering and Manufacturing, 2019, 20(8): 1451-1464 doi: 10.1007/s12541-019-00132-x
    [7]
    顾冬冬, 张红梅, 陈洪宇等. 航空航天高性能金属材料构件激光增材制造. 中国激光, 2020, 47(5): 32-55 (Gu Dongdong, Zhang Hongmei, Chen Hongyu, et al. Laser additive manufacturing of high-performance metallic aerospace components. Chinese Journal of Lasers, 2020, 47(5): 32-55 (in Chinese)

    Gu Dongdong, Zhang Hongmei, Chen Hongyu, et al. Laser additive manufacturing of high-performance metallic aerospace components. Chinese Journal of Lasers, 2020, 47(5): 32-55 (in Chinese)
    [8]
    Edwards P, Ramulu M. Fatigue performance evaluation of selective laser melted Ti–6Al–4V. Materials Science and Engineering A, 2014, 598: 327-337 doi: 10.1016/j.msea.2014.01.041
    [9]
    易敏, 常珂, 梁晨光等. 增材制造微结构演化及疲劳分散性计算. 力学学报, 2021, 53(12): 3263-3273 (Yi Min, Chang Ke, Liang Chenguang, et al. Computational study of evolution and fatigue dispersity of microstructures by additive manufacturing. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(12): 3263-3273 (in Chinese)

    Yi Min, Chang Ke, Liang Chenguang, et al. Computational study of evolution and fatigue dispersity of microstructures by additive manufacturing. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(12): 3263-3273 (in Chinese)
    [10]
    敖晓辉, 刘检华, 夏焕雄等. 选择性激光熔化工艺的介-微观建模与仿真方法综述. 机械工程学报, 2022, 58(5): 239-257 (Ao Xiaohui, Liu Jianhua, Xia Huanxiong, et al. A review of meso-micro modeling and simulation methods of selective laser melting process. Journal of Mechanical Engineering, 2022, 58(5): 239-257 (in Chinese) doi: 10.3901/JME.2022.05.239

    Ao Xiaohui, Liu Jianhua, Xia Huanxiong, et al. A review of meso-micro modeling and simulation methods of selective laser melting process. Journal of Mechanical Engineering, 2022, 58(5): 239-257 (in Chinese) doi: 10.3901/JME.2022.05.239
    [11]
    廉艳平, 王潘丁, 高杰等. 金属增材制造若干关键力学问题研究进展. 力学进展, 2021, 51(3): 648-701 (Lian Yanping, Wang Panding, Gao Jie, et al. Fundamental mechanics problems in metal additive manufacturing: A state-of-art review. Advances in Mechanics, 2021, 51(3): 648-701 (in Chinese)

    Lian Yanping, Wang Panding, Gao Jie, et al. Fundamental mechanics problems in metal additive manufacturing: A state-of-art review. Advances in Mechanics, 2021, 51(3): 648-701 (in Chinese)
    [12]
    温琦, 刘景麟, 孟祥晨等. 搅拌摩擦增材制造关键技术与装备发展. 焊接学报, 2022, 43(6): 1-10, 113 (Wen Qi, Liu Jinglin, Meng Xiangchen, et al. Development in key technique and equipment of friction stir additive manufacturing. Transactions of the China Welding Institution, 2022, 43(6): 1-10, 113 (in Chinese)

    Wen Qi, Liu Jinglin, Meng Xiangchen, et al. Development in key technique and equipment of friction stir additive manufacturing. Transactions of the China Welding Institution, 2022, 43(6): 1-10, 113 (in Chinese)
    [13]
    Wang C, Tan XP, Tor SB, et al. Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 2020, 36: 101538 doi: 10.1016/j.addma.2020.101538
    [14]
    Goh GD, Sing SL, Yeong WY. A review on machine learning in 3D printing: applications, potential, and challenges. Artificial Intelligence Review, 2021, 54(1): 63-94 doi: 10.1007/s10462-020-09876-9
    [15]
    DebRoy T, Mukherjee T, Wei HL, et al. Metallurgy, mechanistic models and machine learning in metal printing. Nature Reviews Materials, 2021, 6(1): 48-68
    [16]
    刘源, 魏世忠. 数据驱动的钢铁耐磨材料性能预测研究综述. 机械工程学报, 2022, 58(10): 31-50 (Liu Yuan, Wei Shizhong. Review on data-driven method for property prediction of iron and steel wear-resistant materials. Journal of Mechanical Engineering, 2022, 58(10): 31-50 (in Chinese) doi: 10.3901/JME.2022.10.031

    Liu Yuan, Wei Shizhong. Review on data-driven method for property prediction of iron and steel wear-resistant materials. Journal of Mechanical Engineering, 2022, 58(10): 31-50 (in Chinese) doi: 10.3901/JME.2022.10.031
    [17]
    Suwardi A, Wang FK, Xue K, et al. Machine learning-driven biomaterials evolution. Advanced Materials, 2022, 34(1): 2102703 doi: 10.1002/adma.202102703
    [18]
    Tyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote) omics data. Nature Methods, 2016, 13(9): 731-740 doi: 10.1038/nmeth.3901
    [19]
    周永章, 陈烁, 张旗等. 大数据与数学地球科学研究进展——大数据与数学地球科学专题代序. 岩石学报, 2018, 34(2): 255-263 (Zhou Yongzhang, Chen Shuo, Zhang Qi, et al. Advances and prospects of big data and mathematical geoscience. Acta Petrologica Sinica, 2018, 34(2): 255-263 (in Chinese)

    Zhou Yongzhang, Chen Shuo, Zhang Qi, et al. Advances and prospects of big data and mathematical geoscience. Acta Petrologica Sinica, 2018, 34(2): 255-263 (in Chinese)
    [20]
    Jung M, Reichstein M, Ciais P, et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 2010, 467(7318): 951-954 doi: 10.1038/nature09396
    [21]
    Guo SH, Agarwal M, Cooper C, et al. Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm. Journal of Manufacturing Systems, 2022, 62: 145-163 doi: 10.1016/j.jmsy.2021.11.003
    [22]
    Giam A, Chen F, Cai JX, et al. Factorial design analytics on effects of material parameter uncertainties in multiphysics modeling of additive manufacturing. NPJ Computational Materials, 2023, 9(1): 1-18 doi: 10.1038/s41524-022-00962-w
    [23]
    Zhan Y, Liu C, Zhang JJ, et al. Measurement of residual stress in laser additive manufacturing TC4 titanium alloy with the laser ultrasonic technique. Materials Science and Engineering A, 2019, 762: 138093 doi: 10.1016/j.msea.2019.138093
    [24]
    Lee JA, Sagong MJ, Jung J, et al. Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing. Journal of Materials Research and Technology, 2023, 22: 413-423 doi: 10.1016/j.jmrt.2022.11.137
    [25]
    Ding DH, He FY, Yuan L, et al. The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration. Journal of Industrial Information Integration, 2021, 23: 100218 doi: 10.1016/j.jii.2021.100218
    [26]
    师彬彬, 陈哲涵. 基于图像特征融合的粉末床缺陷检测方法. 航空学报, 2021, 42(10): 427-438 (Shi Binbin, Chen Zhehan. Defect detection method of powder bed based on image feature fusion. Acta Aeronautica et Astronautica Sinica, 2021, 42(10): 427-438 (in Chinese)

    Shi Binbin, Chen Zhehan. Defect detection method of powder bed based on image feature fusion. Acta Aeronautica et Astronautica Sinica, 2021, 42(10): 427-438 (in Chinese)
    [27]
    Horňas J, Běhal J, Homola P, et al. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. International Journal of Fatigue, 2023, 169: 107483 doi: 10.1016/j.ijfatigue.2022.107483
    [28]
    Gui YW, Aoyagi K, Bian HK, et al. Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam. Additive Manufacturing, 2022, 54: 102736 doi: 10.1016/j.addma.2022.102736
    [29]
    Zhang ZW, Zhang YY, Wen YT, et al. Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures. Complex & Intelligent Systems, 2023, 9(5): 5881-5892
    [30]
    Farias FWC, da Cruz Payão Filho J, Moraes e Oliveira VHP. Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network. Additive Manufacturing, 2021, 48: 102387 doi: 10.1016/j.addma.2021.102387
    [31]
    Elhoone H, Zhang TY, Anwar M, et al. Cyber-based design for additive manufacturing using artificial neural networks for industry 4.0. International Journal of Production Research, 2020, 58(9): 2841-2861 doi: 10.1080/00207543.2019.1671627
    [32]
    Karniadakis GE, Kevrekidis IG, Lu L, et al. Physics-informed machine learning. Nature Reviews Physics, 2021, 3(6): 422-440 doi: 10.1038/s42254-021-00314-5
    [33]
    Wang HJ, Li B, Gong JG, et al. Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods. Engineering Fracture Mechanics, 2023, 284: 109242 doi: 10.1016/j.engfracmech.2023.109242
    [34]
    Zhao L, Song LB, Macías Santos JG, et al. Review on the correlation between microstructure and mechanical performance for laser powder bed fusion AlSi10Mg. Additive Manufacturing, 2022, 56: 102914 doi: 10.1016/j.addma.2022.102914
    [35]
    Macías Santos JG, Douillard T, Zhao L, et al. Influence on microstructure, strength and ductility of build platform temperature during laser powder bed fusion of AlSi10Mg. Acta Materialia, 2020, 201: 231-243 doi: 10.1016/j.actamat.2020.10.001
    [36]
    Khanzadeh M, Chowdhury S, Tschopp MA, et al. In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Transactions, 2019, 51(5): 437-455 doi: 10.1080/24725854.2017.1417656
    [37]
    Lee S, Peng J, Shin D, et al. Data analytics approach for melt-pool geometries in metal additive manufacturing. Science and Technology of Advanced Materials, 2019, 20(1): 972-978 doi: 10.1080/14686996.2019.1671140
    [38]
    Wang Z, Jiang C, Liu PW, et al. Uncertainty quantification and reduction in metal additive manufacturing. NPJ Computational Materials, 2020, 6(1): 1-10 doi: 10.1038/s41524-019-0267-z
    [39]
    Meng LB, Zhang J. Process design of laser powder bed fusion of stainless steel using a gaussian process-based machine learning model. JOM, 2020, 72(1): 420-428 doi: 10.1007/s11837-019-03792-2
    [40]
    Olleak A, Xi ZM. Calibration and validation framework for selective laser melting process based on multi-fidelity models and limited experiment data. Journal of Mechanical Design, 2020, 142(8): 081701 doi: 10.1115/1.4045744
    [41]
    Ren Y, Wang Q. Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion. Journal of Intelligent Manufacturing, 2022, 33(8): 2239-2256 doi: 10.1007/s10845-021-01781-4
    [42]
    Wang Z, Liu PW, Xiao YH, et al. A data-driven approach for process optimization of metallic additive manufacturing under uncertainty. Journal of Manufacturing Science and Engineering, 2019, 141(8): 081004 doi: 10.1115/1.4043798
    [43]
    Li J, Sage M, Guan X, et al. Machine learning-enabled competitive grain growth behavior study in directed energy deposition fabricated Ti-6Al-4V. JOM, 2020, 72(1): 458-464 doi: 10.1007/s11837-019-03917-7
    [44]
    Kats D, Wang ZD, Gan ZT, et al. A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition. Computational Materials Science, 2022, 202: 110958 doi: 10.1016/j.commatsci.2021.110958
    [45]
    Hong RC, Zhang L, Lifton J, et al. Artificial neural network-based geometry compensation to improve the printing accuracy of selective laser melting fabricated sub-millimetre overhang trusses. Additive Manufacturing, 2021, 37: 101594 doi: 10.1016/j.addma.2020.101594
    [46]
    Gan ZT, Li HY, Wolff SJ, et al. Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map. Engineering, 2019, 5(4): 730-735 doi: 10.1016/j.eng.2019.03.014
    [47]
    Wu ZK, Wu SC, Bao JG, et al. The effect of defect population on the anisotropic fatigue resistance of AlSi10Mg alloy fabricated by laser powder bed fusion. International Journal of Fatigue, 2021, 151: 106317 doi: 10.1016/j.ijfatigue.2021.106317
    [48]
    Chen LQ, Yao XL, Xu P, et al. Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning. Virtual and Physical Prototyping, 2021, 16(1): 50-67 doi: 10.1080/17452759.2020.1832695
    [49]
    Li R, Jin M, Paquit VC. Geometrical defect detection for additive manufacturing with machine learning models. Materials & Design, 2021, 206: 109726
    [50]
    Li R, Jin MZ, Pei ZR, et al. Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning. The International Journal of Advanced Manufacturing Technology, 2022, 120(5): 3719-3729
    [51]
    Pandiyan V, Drissi-Daoudi R, Shevchik S, et al. Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions. Virtual and Physical Prototyping, 2021, 16(4): 481-497
    [52]
    Liu R, Liu S, Zhang XL. A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. The International Journal of Advanced Manufacturing Technology, 2021, 113(7): 1943-1958
    [53]
    Zhang ER, Dao M, Karniadakis GE, et al. Analyses of internal structures and defects in materials using physics-informed neural networks. Science Advances, 2022, 8(7): eabk0644 doi: 10.1126/sciadv.abk0644
    [54]
    Akbari P, Ogoke F, Kao NY, et al. MeltpoolNet: Melt pool characteristic prediction in metal additive manufacturing using machine learning. Additive Manufacturing, 2022, 55: 102817 doi: 10.1016/j.addma.2022.102817
    [55]
    Poudel A, Yasin MS, Ye J, et al. Feature-based volumetric defect classification in metal additive manufacturing. Nature Communications, 2022, 13(1): 1-12 doi: 10.1038/s41467-021-27699-2
    [56]
    Demir K, Zhang ZZ, Ben-Artzy A, et al. Laser scan strategy descriptor for defect prognosis in metal additive manufacturing using neural networks. Journal of Manufacturing Processes, 2021, 67: 628-634 doi: 10.1016/j.jmapro.2021.05.011
    [57]
    Wang HJ, Li B, Xuan FZ. A dimensionally augmented and physics-informed machine learning for quality prediction of additively manufactured high-entropy alloy. Journal of Materials Processing Technology, 2022, 307: 117637 doi: 10.1016/j.jmatprotec.2022.117637
    [58]
    Xiong J, Zhang GJ, Hu JW, et al. Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. Journal of Intelligent Manufacturing, 2014, 25(1): 157-163 doi: 10.1007/s10845-012-0682-1
    [59]
    Popova E, Rodgers TM, Gong XY, et al. Process-structure linkages using a data science approach: Application to simulated additive manufacturing data. Integrating Materials and Manufacturing Innovation, 2017, 6(1): 54-68 doi: 10.1007/s40192-017-0088-1
    [60]
    Tapia G, Elwany AH, Sang H. Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Additive Manufacturing, 2016, 12: 282-290 doi: 10.1016/j.addma.2016.05.009
    [61]
    亓欣波, 李长鹏, 李阳等. 基于机器学习的电子束选区熔化成形件密度预测. 机械工程学报, 2019, 55(15): 48-55 (Yuan Xinbo, Li Changpeng, Li Yang, et al. Machine learning algorithms on density prediction of electron beam selective melted parts. Journal of Mechanical Engineering, 2019, 55(15): 48-55 (in Chinese) doi: 10.3901/JME.2019.15.048

    Yuan Xinbo, Li Changpeng, Li Yang, et al. Machine learning algorithms on density prediction of electron beam selective melted parts. Journal of Mechanical Engineering, 2019, 55(15): 48-55 (in Chinese) doi: 10.3901/JME.2019.15.048
    [62]
    Liu Q, Wu HK, Paul MJ, et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms. Acta Materialia, 2020, 201: 316-328 doi: 10.1016/j.actamat.2020.10.010
    [63]
    Shin DS, Lee CH, Kühn U, et al. Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence. Journal of Alloys and Compounds, 2021, 862: 158018 doi: 10.1016/j.jallcom.2020.158018
    [64]
    Liu S, Stebner AP, Kappes BB, et al. Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Additive Manufacturing, 2021, 39: 101877 doi: 10.1016/j.addma.2021.101877
    [65]
    Wu Q, Mukherjee T, De A, et al. Residual stresses in wire-arc additive manufacturing–hierarchy of influential variables. Additive Manufacturing, 2020, 35: 101355 doi: 10.1016/j.addma.2020.101355
    [66]
    Hajializadeh F, Ince A. Integration of artificial neural network with finite element analysis for residual stress prediction of direct metal deposition process. Materials Today Communications, 2021, 27: 102197 doi: 10.1016/j.mtcomm.2021.102197
    [67]
    Park HS, Nguyen DS, Le-Hong T, et al. Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. Journal of Intelligent Manufacturing, 2022, 33(6): 1843-1858 doi: 10.1007/s10845-021-01773-4
    [68]
    Rankouhi B, Jahani S, Pfefferkorn FE, et al. Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters. Additive Manufacturing, 2021, 38: 101836 doi: 10.1016/j.addma.2021.101836
    [69]
    Khorasani AM, Gibson I, Ghasemi A, et al. Modelling of laser powder bed fusion process and analysing the effective parameters on surface characteristics of Ti-6Al-4V. International Journal of Mechanical Sciences, 2020, 168: 105299 doi: 10.1016/j.ijmecsci.2019.105299
    [70]
    Xia CY, Pan ZX, Polden J, et al. Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing, 2022, 33(5): 1467-1482 doi: 10.1007/s10845-020-01725-4
    [71]
    Özel T, Altay A, Kaftanoğlu B, et al. Focus variation measurement and prediction of surface texture parameters using machine learning in laser powder bed fusion. Journal of Manufacturing Science and Engineering, 2020, 142(1): 011008 doi: 10.1115/1.4045415
    [72]
    Hertlein N, Deshpande S, Venugopal V, et al. Prediction of selective laser melting part quality using hybrid Bayesian network. Additive Manufacturing, 2020, 32: 101089 doi: 10.1016/j.addma.2020.101089
    [73]
    Yadollahi A, Shamsaei N. Additive manufacturing of fatigue resistant materials: Challenges and opportunities. International Journal of Fatigue, 2017, 98: 14-31 doi: 10.1016/j.ijfatigue.2017.01.001
    [74]
    杨天雨, 张鹏林, 尹燕等. 激光选区熔化组织分析及人工神经网络力学性能预测. 焊接学报, 2019, 40(6): 100-106, 165-166 (Yang Tianyu, Zhang Penglin, Yin Yan, et al. Microstructure based on selective laser melting and mechanical properties prediction through artificial neural net. Transactions of the China Welding Institution, 2019, 40(6): 100-106, 165-166 (in Chinese)

    Yang Tianyu, Zhang Penglin, Yin Yan, et al. Microstructure based on selective laser melting and mechanical properties prediction through artificial neural net. Transactions of the China Welding Institution, 2019, 40(6): 100-106, 165-166 (in Chinese)
    [75]
    Li JC, Cao LC, Hu JX, et al. A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength. Journal of Intelligent Manufacturing, 2022, 33(3): 687-702 doi: 10.1007/s10845-020-01665-z
    [76]
    Huang DJ, Li H. A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Materials & Design, 2021, 203: 109606
    [77]
    Sanchez S, Rengasamy D, Hyde CJ, et al. Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing, 2021, 32(8): 2353-2373 doi: 10.1007/s10845-021-01785-0
    [78]
    Kusano M, Miyazaki S, Watanabe M, et al. Tensile properties prediction by multiple linear regression analysis for selective laser melted and post heat-treated Ti-6Al-4V with microstructural quantification. Materials Science and Engineering A, 2020, 787: 139549 doi: 10.1016/j.msea.2020.139549
    [79]
    Muhammad W, Brahme AP, Ibragimova O, et al. A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys. International Journal of Plasticity, 2021, 136: 102867 doi: 10.1016/j.ijplas.2020.102867
    [80]
    Hu E, Seetoh IP, Lai CQ. Machine learning assisted investigation of defect influence on the mechanical properties of additively manufactured architected materials. International Journal of Mechanical Sciences, 2022, 221: 107190 doi: 10.1016/j.ijmecsci.2022.107190
    [81]
    Yan F, Chan YC, Saboo A, et al. Data-driven prediction of mechanical properties in support of rapid certification of additively manufactured alloys. Computer Modeling in Engineering & Sciences, 2018, 117(3): 343-366
    [82]
    Dan CY, Cui YC, Wu Y, et al. Achieving ultrahigh fatigue resistance in AlSi10Mg alloy by additive manufacturing. Nature Materials, 2023, 22: 1182-1188 doi: 10.1038/s41563-023-01651-9
    [83]
    Zerbst U, Bruno G, Buffiere JY, et al. Damage tolerant design of additively manufactured metallic components subjected to cyclic loading: State of the art and challenges. Progress in Materials Science, 2021, 121: 100786 doi: 10.1016/j.pmatsci.2021.100786
    [84]
    轩福贞, 朱明亮, 王国彪. 结构疲劳百年研究的回顾与展望. 机械工程学报, 2021, 57(6): 25-61 (Xuan Fuzhen, Zhu Mingliang, Wang Guobiao. Retrospect and prospect on century-long research of structural fatigue. Journal of Mechanical Engineering, 2021, 57(6): 25-61 (in Chinese)

    Xuan Fuzhen, Zhu Mingliang, Wang Guobiao. Retrospect and prospect on century-long research of structural fatigue. Journal of Mechanical Engineering, 2021, 57(6): 25-61 (in Chinese)
    [85]
    Mortazavi SNS, Ince A. An artificial neural network modeling approach for short and long fatigue crack propagation. Computational Materials Science, 2020, 185: 109962 doi: 10.1016/j.commatsci.2020.109962
    [86]
    Himmiche S, Mortazavi SNS, Ince A. Comparative study of neural network-based models for fatigue crack growth predictions of short cracks. Journal of Peridynamics and Nonlocal Modeling, 2022, 4(4): 501-526 doi: 10.1007/s42102-021-00062-1
    [87]
    Zhang M, Sun CN, Zhang X, et al. High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach. International Journal of Fatigue, 2019, 128: 105194 doi: 10.1016/j.ijfatigue.2019.105194
    [88]
    Vassilopoulos AP, Bedi R. Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates. Computational Materials Science, 2008, 43(4): 1086-1093 doi: 10.1016/j.commatsci.2008.02.028
    [89]
    Luo YW, Zhang B, Feng X, et al. Pore-affected fatigue life scattering and prediction of additively manufactured inconel 718: An investigation based on miniature specimen testing and machine learning approach. Materials Science and Engineering A, 2021, 802: 140693 doi: 10.1016/j.msea.2020.140693
    [90]
    Bao HYX, Wu SC, Wu ZK, et al. A machine-learning fatigue life prediction approach of additively manufactured metals. Engineering Fracture Mechanics, 2021, 242: 107508 doi: 10.1016/j.engfracmech.2020.107508
    [91]
    Li J, Yang ZM, Qian GA, et al. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting. International Journal of Fatigue, 2022, 158: 106764 doi: 10.1016/j.ijfatigue.2022.106764
    [92]
    Shi T, Sun JY, Li JH, et al. Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting. International Journal of Fatigue, 2023, 171: 107585 doi: 10.1016/j.ijfatigue.2023.107585
    [93]
    Peng X, Wu SC, Qian WJ, et al. The potency of defects on fatigue of additively manufactured metals. International Journal of Mechanical Sciences, 2022, 221: 107185 doi: 10.1016/j.ijmecsci.2022.107185
    [94]
    Li AY, Baig S, Shao S, et al. Defect criticality analysis on fatigue life of L-PBF 17-4 PH stainless steel via machine learning. International Journal of Fatigue, 2022, 163: 107018 doi: 10.1016/j.ijfatigue.2022.107018
    [95]
    Cutolo A, Lammens N, Boer KV, et al. Fatigue life prediction of a L-PBF component in Ti-6Al-4V using sample data, FE-based simulations and machine learning. International Journal of Fatigue, 2023, 167: 107276 doi: 10.1016/j.ijfatigue.2022.107276
    [96]
    Zhan ZX, Ao N, Hu YN, et al. Defect-induced fatigue scattering and assessment of additively manufactured 300M-AerMet100 steel: An investigation based on experiments and machine learning. Engineering Fracture Mechanics, 2022, 264: 108352 doi: 10.1016/j.engfracmech.2022.108352
    [97]
    Wang HJ, Li B, Xuan FZ. Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features. International Journal of Fatigue, 2022, 164: 107147 doi: 10.1016/j.ijfatigue.2022.107147
    [98]
    Ciampaglia A, Tridello A, Paolino DS, et al. Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts. International Journal of Fatigue, 2023, 170: 107500 doi: 10.1016/j.ijfatigue.2023.107500
    [99]
    Chen J, Liu Y. Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning. Additive Manufacturing, 2021, 39: 101876 doi: 10.1016/j.addma.2021.101876
    [100]
    Yu H, Hu YN, Kang GZ, et al. High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: A domain knowledge-guided symbolic regression approach. Philosophical Transactions of the Royal Society A, 2023, 381: 20220383
    [101]
    Wang LY, Zhu SP, Luo CQ, et al. Physics-guided machine learning frameworks for fatigue life prediction of AM materials. International Journal of Fatigue, 2023, 172: 107658 doi: 10.1016/j.ijfatigue.2023.107658
    [102]
    Gan L, Wu H, Zhong Z. Integration of symbolic regression and domain knowledge for interpretable modeling of remaining fatigue life under multistep loading. International Journal of Fatigue, 2022, 161: 106889 doi: 10.1016/j.ijfatigue.2022.106889
    [103]
    Salvati E, Tognan A, Laurenti L, et al. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing. Materials & Design, 2022, 222: 111089
    [104]
    Wang LY, Zhu SP, Luo CQ, et al. Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials. Philosophical Transactions of the Royal Society A, 2023, 381(2260): 20220386 doi: 10.1098/rsta.2022.0386
    [105]
    Xie XY, Bennett J, Saha S, et al. Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing. NPJ Computational Materials, 2021, 7(1): 1-12 doi: 10.1038/s41524-020-00473-6
    [106]
    Porro M, Zhang B, Parmar A, et al. Data-driven modeling of mechanical properties for 17-4 PH stainless steel built by additive manufacturing. Integrating Materials and Manufacturing Innovation, 2022, 11(2): 241-255 doi: 10.1007/s40192-022-00261-8
    [107]
    Hassanin H, Alkendi Y, Elsayed M, et al. Controlling the properties of additively manufactured cellular structures using machine learning approaches. Advanced Engineering Materials, 2020, 22(3): 1901338 doi: 10.1002/adem.201901338
    [108]
    Herriott C, Spear AD. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Computational Materials Science, 2020, 175: 109599 doi: 10.1016/j.commatsci.2020.109599
    [109]
    Zhan ZX, Li H. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing. International Journal of Fatigue, 2021, 145: 106089 doi: 10.1016/j.ijfatigue.2020.106089
    [110]
    Zhan ZX, Li H. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. International Journal of Fatigue, 2021, 142: 105941 doi: 10.1016/j.ijfatigue.2020.105941
    [111]
    Elangeswaran C, Cutolo A, Gallas S, et al. Predicting fatigue life of metal LPBF components by combining a large fatigue database for different sample conditions with novel simulation strategies. Additive Manufacturing, 2022, 50: 102570 doi: 10.1016/j.addma.2021.102570
    [112]
    Moon S, Ma RM, Attardo R, et al. Impact of surface and pore characteristics on fatigue life of laser powder bed fusion Ti–6Al–4V alloy described by neural network models. Scientific Reports, 2021, 11(1): 20424 doi: 10.1038/s41598-021-99959-6
    [113]
    Zhan ZX, Hu WP, Meng QC. Data-driven fatigue life prediction in additive manufactured titanium alloy: A damage mechanics based machine learning framework. Engineering Fracture Mechanics, 2021, 252: 107850 doi: 10.1016/j.engfracmech.2021.107850
    • Related Articles

  • Related Articles

    [1]Luo Cheng, Yuan Huang. FATIGUE LIFE EVALUATION OF RAFTED NICKEL-BASE SINGLE CRYSTAL SUPERALLOYS BASED ON TENSORIAL MICROSTRUCTURE CHARACTERIZATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 2029-2050. DOI: 10.6052/0459-1879-23-609
    [2]Li Wenqi, Xiang Zhong, Li Haoran, Ren Zhongkai, Wang Jiadong. STUDY ON THE PREDICTION MODEL OF THE INTRINSIC DAMAGE DISSIPATION LIFE OF TWO-STAGE VARIABLE AMPLITUDE STRAIN FATIGUE[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(1): 149-156. DOI: 10.6052/0459-1879-23-314
    [3]Chen Haihua, Zhang Xianfeng, Zhao Wenjie, Gao Zhilin, Liu Chuang, Tan Mengting, Xiong Wei, Wang Haiying, Dai Lanhong. EFFECT OF MICROSTRUCTURE ON FLOW BEHAVIOR DURING PENETRATION OF W25Fe25Ni25Mo25 HIGH-ENTROPY ALLOY PROJECTILE[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(8): 2140-2151. DOI: 10.6052/0459-1879-22-128
    [4]Li Jing, Tong Xiaolong, Yang Shuo, Qiu Yuanying. A MULTIAXIAL HIGH CYCLE FATIGUE LIFE PREDICTION MODEL CONSIDERING THE EFFECT OF MEAN STRESS FOR TENSION-TORSION LOADINGS WITH SAME FREQUENCY[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1409-1421. DOI: 10.6052/0459-1879-20-066
    [5]Zhao Ernian, Qu Weilian. A NEW PROPOSAL FOR MULTIAXIAL LOW-CYCLE FATIGUE LIFE PREDICTION UNDER NON-PROPORTIONAL LOADING[J]. Chinese Journal of Theoretical and Applied Mechanics, 2016, 48(4): 944-952. DOI: 10.6052/0459-1879-15-377
    [6]Jiang Chao, Deng Qun, Li Bochuan. A NEW MULTIAXIAL FATIGUE LIFE PREDICTION MODEL BASED ON THE NONPROPORTIONAL ADDITIONAL DAMAGE[J]. Chinese Journal of Theoretical and Applied Mechanics, 2015, 47(4): 634-641. DOI: 10.6052/0459-1879-15-050
    [7]Li Yuan, Han Xu, Liu Jie, Jiang Chao. A PREDICTION METHOD ON HIGH-CYCLE FATIGUE PARAMETERS BASED ON DISSIPATED ENERGY COMPUTATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2013, 45(3): 367-374. DOI: 10.6052/0459-1879-12-300
    [8]A new model for life prediction of multiaxial high-cycle fatigue[J]. Chinese Journal of Theoretical and Applied Mechanics, 2010, 42(6): 1225-1230. DOI: 10.6052/0459-1879-2010-6-lxxb2009-655
    [9]THE EFFECT OF PROPORTIONAL OVERLOAD ON THE LOW CYCLE TENSION-TENSION FATIGUE LIFE OF ALUMINIUM ALLOY PLATE WITH CIRCULAR HOLE[J]. Chinese Journal of Theoretical and Applied Mechanics, 1993, 25(4): 492-495. DOI: 10.6052/0459-1879-1993-4-1995-670
    [10]A CLOSED FORM SOLUTION ABOUT HIGH CYCLE FATIGUE CRACK INITIATION LIVES OF NOTCHED PLATES BY METHODOLOGY OF DAMAGE MECHANICS[J]. Chinese Journal of Theoretical and Applied Mechanics, 1993, 25(4): 452-459. DOI: 10.6052/0459-1879-1993-4-1995-665

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (880) PDF downloads (320) Cited by()
    Related

    Download Center

    Author Center

    Copyright © Editorial Office of the Chinese Journal of Mechanics   京ICP备05039218号-1

    Address：15 Beishihuan Xi Lu, Haidian District, Beijing, China   China Pos：100190

    Tel：010-62536271

    Email：lxxb@cstam.org.cn

    微信公众号
    RSS

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return