| Citation: |
HE Mingyang, FU Guang, JIN Shangkun, et al. Research progress in prediction of mechanical properties of porous materials based on machine learning[J]. Acta Materiae Compositae Sinica.
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Porous materials have attracted much attention due to their wide potential applications. Traditionally, research on the mechanical properties of porous materials has mainly relied on time-consuming and cumbersome experimental and theoretical analysis methods. In recent years, machine learning technology has provided an efficient solution to simplify the complex relationship between porous material parameters and mechanical properties. This article reviews the latest research progress of machine learning in predicting the mechanical properties of porous materials. Firstly, commonly used machine learning algorithms were introduced, with a focus on analyzing the application of neural network prediction methods in this field. This method was summarized into three major strategies: mechanism model driven neural network, integration of neural networks and mechanistic model, and integration of neural network and optimization technology. Then, the basic principles and applications of the above strategies were analyzed in detail. Finally, we discussed how to develop more efficient hybrid models by improving neural network technology and integrating it with optimization algorithms, and looked forward to the development prospects of neural networks in this field.
Porous materials have received high attention in the fields of energy absorption, shock absorption, and thermal insulation due to their unique physical properties and wide application prospects. However, traditional experimental testing and theoretical analysis methods face problems such as high cost, complexity, and computational time when studying the mechanical properties of porous materials. In recent years, machine learning, especially the application of neural networks, has provided efficient solutions for predicting the mechanical properties of porous materials. Exploring the application and effectiveness of neural networks in the performance evaluation of porous materials has important academic and practical significance.
Given the complexity of porous material structures and their optimization requirements in material design, this paper summarizes the strategies for applying neural networks to predict the mechanical properties of porous materials into three categories: (1) mechanism model driven neural networks, which utilize data-driven neural networks generated based on mechanism models to provide prior knowledge through physical models and guide neural networks to handle complex nonlinear problems; (2) Integrating mechanism models with neural networks, by embedding physical information to constrain the model in limited data, significantly improves the accuracy, efficiency, and physical precision of the model, making it perform better in handling complex physical problems; (3) The integration of neural networks and optimization techniques, combined with optimization algorithms such as genetic algorithms, enhances the global search capability of neural networks, improves predictive performance and computational efficiency.
In practical applications, the three strategies demonstrated different advantages. The mechanism model driven neural network strategy can generate high-quality training data through theoretical models under limited data or experimental conditions, thereby improving the model's generalization ability and prediction accuracy, especially suitable for porous material systems with clear physical laws. The integration strategy of mechanism models and neural networks is suitable for dealing with highly nonlinear or physically complex porous material problems. By combining traditional mechanism models with neural networks, it is possible to improve computational efficiency while maintaining strong interpretability, making it particularly suitable for topology optimization and solving complex differential equations. The integration strategy of neural networks and optimization techniques performs well in multi-objective optimization and reverse engineering design. By integrating with optimization algorithms such as genetic algorithms, it can achieve global optimal solutions under multiple constraints, especially in the trade-off between material design and performance optimization, which has significant advantages. Therefore, selecting appropriate strategies based on the complexity of different problems and data conditions can maximize the effectiveness of porous material design and performance prediction.Conclusions: Although neural networks have made significant progress in predicting the mechanical properties of porous materials, they still face challenges such as data quality, model interpretability, and generalization ability. Future research should focus on improving neural network technology and combining optimization algorithms to develop more efficient hybrid models, providing more reliable and accurate solutions for the engineering applications of porous materials.
| [1] |
PAN C, HAN Y, LU J. Design and optimization of lattice structures: A review[J]. Applied Sciences, 2020, 10(18): 6374. DOI: 10.3390/app10186374
|
| [2] |
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[J]. 中国激光, 2020, 47(5): 32-55.
GU Dongdong, ZHANG Hongmei, CHEN Hongyu, et al. Laser additive manufacturing of high-performance metal material components for aerospace[J]. Chinese Laser, 2020, 47(5): 32-55(in Chinese).
|
| [3] |
QIAN B, ZHANG L, FAN H, MAO J, et a. The mechanical characteristics and cooling performance of a turbine blade with non-homogeneous lattice structure[J]. Materials Today Communications, 2024, 38: 108247. DOI: 10.1016/j.mtcomm.2024.108247
|
| [4] |
DONG G, TANG Y, ZHAO Y F. A survey of modeling of lattice structures fabricated by additive manufacturing[J]. Journal of Mechanical Design, 2017, 139(10): 100906.
|
| [5] |
WANG G, ZHAO J, WANG G, et al. Advances in polymer blending and composites for sustainable development[J]. European Polymer Journal, 2017, 95: 382-393. DOI: 10.1016/j.eurpolymj.2017.08.025
|
| [6] |
HU H, WANG W, NING H, et al. Numerical simulation and experimental study on the compression performance of a double layer cross-lattice-core sandwich structure[J]. Structures, 2024, 64: 106531. DOI: 10.1016/j.istruc.2024.106531
|
| [7] |
YIN H, ZHANG W, ZHU L, et al. Review on lattice structures for energy absorption properties[J]. Composite Structures, 2023, 304: 116397. DOI: 10.1016/j.compstruct.2022.116397
|
| [8] |
WANG C, XIE Y M, ZHUANG Z, et al. Strength design of porous materials using B-spline based level set method[J]. Computer Methods in Applied Mechanics and Engineering, 2024, 418: 116490.
|
| [9] |
BOHARA R P, ZHANG T, KIM J, et al. Anti-blast and impact performances of auxetic structures: A review of structures, materials, methods, and fabrications[J]. Engineering Structures, 2023, 276: 115377. DOI: 10.1016/j.engstruct.2022.115377
|
| [10] |
SCHAEDLER T A, RO C J, SORENSEN AE, et al. Designing metallic microlattices for energy absorber applications[J]. Advanced Engineering Materials, 2014, 16(3): 276-283. DOI: 10.1002/adem.201300206
|
| [11] |
NIKNAM H, AKBARZADEH AH. Graded lattice structures: Simultaneous enhancement in stiffness and energy absorption[J]. Materials & Design, 2020, 196: 109129.
|
| [12] |
MALONEY K J, FINK K D, SCHAEDLER T A, et al. Multifunctional heat exchangers derived from three-dimensional micro-lattice structures[J]. International Journal of Heat and Mass Transfer, 2012, 55(9-10): 2486-2493. DOI: 10.1016/j.ijheatmasstransfer.2012.01.011
|
| [13] |
LE V T, HA N S, GOO N S. Advanced sandwich structures for thermal protection systems in hypersonic vehicles: A review[J]. Composites Part B: Engineering, 2021, 226: 109301. DOI: 10.1016/j.compositesb.2021.109301
|
| [14] |
EKADE P, KRISHNAN S. Fluid flow and heat transfer characteristics of octet truss lattice geometry[J]. International Journal of Thermal Sciences, 2019, 137: 253-261. DOI: 10.1016/j.ijthermalsci.2018.11.031
|
| [15] |
ATTARZADEH R, ROVIRA M, DUWIG C. Design analysis of the “Schwartz D” based heat exchanger: A numerical study[J]. International Journal of Heat and Mass Transfer, 2021, 177: 121415.
|
| [16] |
ZHANG L, YANG G, JOHNSON B N, et al. Three-dimensional (3D) printed scaffold and material selection for bone repair[J]. Acta Biomaterialia, 2019, 84: 16-33. DOI: 10.1016/j.actbio.2018.11.039
|
| [17] |
JETTÉ B, BRAILOVSKI V, DUMAS M, et al. Femoral stem incorporating a diamond cubic lattice structure: Design, manufacture and testing[J]. Journal of the Mechanical Behavior of Biomedical Materials, 2018, 77: 58-72. DOI: 10.1016/j.jmbbm.2017.08.034
|
| [18] |
DUMAS M, TERRIAULT P, BRAILOVSKI V. Modelling and characterization of a porosity graded lattice structure for additively manufactured biomaterials[J]. Materials & Design, 2017, 121: 383-392.
|
| [19] |
AL-KETAN O, ROWSHAN R, AL-RUB RKA. Topology-mechanical property relationship of 3D printed strut, skeletal, and sheet based periodic metallic cellular materials[J]. Additive Manufacturing, 2018, 19: 167-183.
|
| [20] |
CHEN W M, LEE S J, LEE PVS. Failure analysis of an additive manufactured porous titanium structure for orthopedic implant applications[C]//Proceedings of the Materials Science Forum, 2016: 45-49.
|
| [21] |
GORGULUARSLAN R M, CHOI S K, SALDANA C J. Uncertainty quantification and validation of 3D lattice scaffolds for computer-aided biomedical applications[J]. Journal of the Mechanical Behavior of Biomedical Materials, 2017, 71: 428-440. DOI: 10.1016/j.jmbbm.2017.04.011
|
| [22] |
SORO N, ATTAR H, BRODIE E, et al. Molotnikov A, Dargusch MS. Evaluation of the mechanical compatibility of additively manufactured porous Ti–25Ta alloy for load-bearing implant applications[J]. Journal of the Mechanical Behavior of Biomedical Materials, 2019, 97: 149-158. DOI: 10.1016/j.jmbbm.2019.05.019
|
| [23] |
RAHMANI R, ANTONOV M, KOLLO L, et al. Mechanical behavior of Ti6Al4V scaffolds filled with CaSiO3 for implant applications[J]. Applied Sciences, 2019, 9(18): 3844.
|
| [24] |
CHAI Y, DU S, LI F, et al. Vibration characteristics of simply supported pyramidal lattice sandwich plates on elastic foundation: Theory and experiments[J]. Thin-Walled Structures, 2021, 166: 108116. DOI: 10.1016/j.tws.2021.108116
|
| [25] |
XU G D, ZENG T, CHENG S, et al. Free vibration of composite sandwich beam with graded corrugated lattice core[J]. Composite Structures, 2019, 229: 111466. DOI: 10.1016/j.compstruct.2019.111466
|
| [26] |
HAN B, QIN K K, ZHANG Q C, et al. Free vibration and buckling of foam-filled composite corrugated sandwich plates under thermal loading[J]. Composite Structures, 2017, 172: 173-189. DOI: 10.1016/j.compstruct.2017.03.051
|
| [27] |
CHENG L, LIANG X, BELSKI E, et al. Natural frequency optimization of variable-density additive manufactured lattice structure: Theory and experimental validation[J]. Journal of Manufacturing Science and Engineering, 2018, 140(10): 105002.
|
| [28] |
SALEHIPOUR H, SHAHMOHAMMADI M A, CIVALEK Ö. Natural frequencies and modal shapes of folded sandwich plates made of porous core and FG-CNTRC coating layers resting on two parameters elastic foundation[J]. Aerospace Science and Technology, 2024, 148: 109077. DOI: 10.1016/j.ast.2024.109077
|
| [29] |
HA N H, TAN N C, DZUNG N M, et al. A new study for dynamical characteristics of double-variable-edge and variable thickness plates made of open-cell porous metal[J]. Aerospace Science and Technology, 2024, 145: 108830.
|
| [30] |
SAMIPOUR S, KHALIULIN V, BATRAKOV V. Manufacturing technology of braided lattice aircraft structures[J]. Journal of Machinery Manufacture and Reliability, 2020, 49: 787-795. DOI: 10.3103/S1052618820090101
|
| [31] |
TANCOGNE-DEJEAN T, DIAMANTOPOULOU M, GORJI M B, et al. 3D plate-lattices: An emerging class of low-density metamaterial exhibiting optimal isotropic stiffness[J]. Advanced Materials, 2018, 30(45): 1803334. DOI: 10.1002/adma.201803334
|
| [32] |
HORAK Z, DVORAK K, ZARYBNICKA L, et al. Experimental measurements of mechanical properties of pur foam used for testing medical devices and instruments depending on temperature, density and strain rate[J]. Materials, 2020, 13(20): 4560.
|
| [33] |
RAGHAVENDRA S, MOLINARI A, FONTANARI V, et al. Tensile and compression properties of variously arranged porous Ti-6Al-4V additively manufactured structures via SLM[J]. Procedia Structural Integrity, 2018, 13: 149-154. DOI: 10.1016/j.prostr.2018.12.025
|
| [34] |
GUHA I, ZHANG X, RAJAPAKSE C S, et al. Finite element analysis of trabecular bone microstructure using CT imaging and continuum mechanical modeling[J]. Medical Physics, 2022, 49(6): 3886-3899. DOI: 10.1002/mp.15629
|
| [35] |
DE GALARRETA S R, JEFFERS J R T, GHOUSE S A validated finite element analysis procedure for porous structures[J]. Materials & Design, 2020, 189: 108027.
|
| [36] |
RAMPRASAD R, BATRA R, PILANIA G, et al. Machine learning in materials informatics: recent applications and prospects[J]. Computational Materials, 2017, 3(1): 54. DOI: 10.1038/s41524-017-0056-5
|
| [37] |
WU Z, WU Y, WENG L, et al. Machine learning approach to predicting the macro-mechanical properties of rock from the meso-mechanical parameters[J]. Computers and Geotechnics, 2024, 166: 105933. DOI: 10.1016/j.compgeo.2023.105933
|
| [38] |
WU K, MENG Q, LI R, et al. A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2023, 15(3): 643-657.
|
| [39] |
SCHMIDT J, MARQUES M R, BOTTI S, et al. Recent advances and applications of machine learning in solid-state materials science[J]. Computational Materials, 2019, 5(1): 83 DOI: 10.1038/s41524-019-0221-0
|
| [40] |
SAMAEI A, CHAUDHURI S. Mechanical performance of zirconia-silica bilayer coating on aluminum alloys with varying porosities: Deep learning and microstructure-based FEM[J]. Materials & Design, 2021, 207: 109860.
|
| [41] |
SHI L, ZHANG J, ZHU Q, et al. Prediction of mechanical behavior of rocks with strong strain-softening effects by a deep-learning approach[J]. Computers and Geotechnics, 2022, 152: 105040. DOI: 10.1016/j.compgeo.2022.105040
|
| [42] |
KELEŞ Ö, HE Y, SIRKECI-MERGEN B. Prediction of elastic stresses in porous materials using fully convolutional networks[J]. Scripta Materialia, 2021, 197: 113805.
|
| [43] |
刘振涛. 机器学习辅助超弹性多孔材料的结构设计 [D]. 西南科技大学, 2024.
LIU Zhentao. Structure design of hyperelastic porous materials assisted by machine learning [D]. Southwest University of Science and Technology, 2024 (in Chinese).
|
| [44] |
WHITE D A, ARRIGHI W J, KUDO J, et al. A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing[J]. Computer Methods in Applied Mechanics and Engineering, 2019, 346: 1118-1135. DOI: 10.1016/j.cma.2018.09.007
|
| [45] |
LIU R, LIU S, ZHANG X. Multiscale topology optimization using neural network surrogate models[J]. The International Journal of Advanced Manufacturing Technology, 2021, 113(7): 1943-1958.
|
| [46] |
肖力, 曹志刚, 卢浩冉, 等. 基于深度学习和梯度优化的弹性超材料设计[J]. 浙江大学学报, 2024, 58(9): 1892-1901.
XIAO Li, CAO Zhigang, LU Haoran, et al. Design of elastic metamaterials based on deep learning and gradient optimization[J]. Journal of Zhejiang University, 2024, 58(9): 1892-1901(in Chinese).
|
| [47] |
DONOHO D. 50 years of data science[J]. Journal of Computational and Graphical Statistics, 2017, 26(4): 745-766.
|
| [48] |
ZHONG S, ZHANG K, WANG D, et al. Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds[J]. Chemical Engineering Journal, 2021, 405: 126627. DOI: 10.1016/j.cej.2020.126627
|
| [49] |
LU Y T, HUO Y, YANG Z Y, et al. Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure[J]. Frontiers in Bioengineering and Biotechnology, 2022, 10: 985688. DOI: 10.3389/fbioe.2022.985688
|
| [50] |
CAI J, CHU X, XU K, et al. Machine learning-driven new material discovery[J]. Nanoscale Advances, 2020, 2(8): 3115-3130.
|
| [51] |
MORALES ESCALANTE. A brief introduction to supervised, unsupervised, and reinforcement learning[M]. Academic Press, USA, 2022: 111-129.
|
| [52] |
胡雅楠, 余欢, 吴圣川, 等. 基于机器学习的增材制造合金材料力学性能预测研究进展与挑战[J]. 力学学报, 2024, 56(7): 1892-1915. DOI: 10.6052/0459-1879-23-542
HU Yanan, YU Huan, WU Shengchuan, et al. Research progress and challenges in predicting the mechanical properties of additive manufacturing alloy materials based on machine learning[J]. Journal of Mechanics, 2024, 56(7): 1892-1915(in Chinese). DOI: 10.6052/0459-1879-23-542
|
| [53] |
SUTHARAHAN S. Machine learning models and algorithms for big data classification[M]. Netherlands: Springer, 2016: 207-235.
|
| [54] |
FUCHS K, GERTHEISS J, TUTZ G. Nearest neighbor ensembles for functional data with interpretable feature selection[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 146: 186-197. DOI: 10.1016/j.chemolab.2015.04.019
|
| [55] |
ZHANG X, WANG Y, ZHANG J. A review of machine learning methods for brain tumor segmentation[J]. Frontiers in Oncology, 2022, 12: 851453.
|
| [56] |
HORŇAS J, BĚHAL J, HOMOLA P, et al. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach[J]. International Journal of Fatigue, 2023, 169: 107483. DOI: 10.1016/j.ijfatigue.2022.107483
|
| [57] |
WU C, LUO J, ZHONG J, et al. Topology optimization for design and additive manufacturing of functionally graded lattice structures using derivative-aware machine learning algorithms[J]. Additive Manufacturing, 2023, 78: 103833.
|
| [58] |
YU Y, HUR T, JUNG J, et al. Deep learning for determining a near-optimal topological design without any iteration[J]. Structural and Multidisciplinary Optimization, 2019, 59(3): 787-799. DOI: 10.1007/s00158-018-2101-5
|
| [59] |
SHEN Z, YANG H, ZHANG S. Neural network approximation: three hidden layers are enough[J]. Neural Networks, 2021, 141: 160-173. DOI: 10.1016/j.neunet.2021.04.011
|
| [60] |
CINAR A C. Training feed-forward multi-layer perceptron artificial neural networks with a tree-seed algorithm[J]. Arabian Journal for Science and Engineering, 2020, 45(12): 10915-10938. DOI: 10.1007/s13369-020-04872-1
|
| [61] |
WU Y C, FENG J W. Development and application of artificial neural network[J]. Wireless Personal Communications, 2018, 102: 1645-1656.
|
| [62] |
MORÉ J J. The Levenberg-Marquardt algorithm: implementation and theory[C]. Proceedings of the Biennial Conference, 2006: 105-116.
|
| [63] |
WU J, CHEN X Y, ZHANG H, et al. Hyperparameter optimization for machine learning models based on Bayesian optimization[J]. Journal of Electronic Science and Technology, 2019, 17(1): 26-40.
|
| [64] |
STERKENBURG T F, GRÜNWALD P D. The no-free-lunch theorems of supervised learning[J]. Synthese, 2021, 199(3): 9979-10015.
|
| [65] |
HOOSHMAND M J, SAKIB UZ-ZAMAN C, KHONDOKER M A H. Machine learning algorithms for predicting mechanical stiffness of lattice structure-based polymer foam[J]. Materials, 2023, 16(22): 7173. DOI: 10.3390/ma16227173
|
| [66] |
LIU Y, SUN F, CHEN M, et al. Prediction of equivalent elastic modulus for metal-coated lattice based on machine learning[J]. Applied Composite Materials, 2023, 30(4): 1207-1229.
|
| [67] |
KARAKOÇ A, KELEŞ Ö. A predictive failure framework for brittle porous materials via machine learning and geometric matching methods[J]. Journal of Materials Science, 2020, 55(11): 4734-4747. DOI: 10.1007/s10853-019-04339-1
|
| [68] |
STEL'MAKH S A, SHCHERBAN E M, BESKOPYLNY A N, et al. Prediction of mechanical properties of highly functional lightweight fiber-reinforced concrete based on deep neural network and ensemble regression trees methods[J]. Materials, 2022, 15(19): 6740. DOI: 10.3390/ma15196740
|
| [69] |
GONG, Y W, MOHAMED M, ILHAM El-M, et al. Machine learning for estimating rock mechanical properties beyond traditional considerations[C]. Proceedings of Unconventional Resources Technology Conference, 2019, 466-480
|
| [70] |
AKKAD K, MEHBOOB H, ALYAMANI R, et al. A machine-learning-based approach for predicting the mechanical performance of semi-porous hip stems[J]. Journal of Functional Biomaterials, 2023, 14(3): 156. DOI: 10.3390/jfb14030156
|
| [71] |
WEI M D, MENG W Z, DAI F, et al. Application of machine learning in predicting the rate-dependent compressive strength of rocks[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(5): 1356-1365.
|
| [72] |
MATIN S S, FARAHZADI L, MAKAREMI S, et al. Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest[J]. Applied Soft Computing, 2018, 70: 980-987. DOI: 10.1016/j.asoc.2017.06.030
|
| [73] |
SUN G R, DU M H, BAO H S, et al. Ultra-high performance concrete design method based on machine learning model and steel slag powder[J]. Case Studies in Construction Materials, 2022, 17: e01682. DOI: 10.1016/j.cscm.2022.e01682
|
| [74] |
LI W, TAN Z Y. Research on rock strength prediction based on least squares support vector machine[J]. Geotechnical and Geological Engineering, 2017, 35: 385-393. DOI: 10.1007/s10706-016-0114-7
|
| [75] |
GAO S L, YUE X Z, WANG H. Predictability of different machine learning approaches on the fatigue life of additive-manufactured porous titanium structure[J]. Metals, 2024, 14(3): 320. DOI: 10.3390/met14030320
|
| [76] |
DESPRÉS N, CYR E, SETOODEH P, et al. Evolutionary computation to design additively manufactured optimal heterogeneous lattice structures[J]. Progress in Additive Manufacturing, 2023, 8(3): 615-627. DOI: 10.1007/s40964-022-00352-0
|
| [77] |
PENG X L, XU B X. Data-driven inverse design of composite triangular lattice structures[J]. International Journal of Mechanical Sciences, 2024, 265: 108900. DOI: 10.1016/j.ijmecsci.2023.108900
|
| [78] |
HE J, KUSHWAHA S, ABUEIDDA D, et al. Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks[J]. Computers & Structures, 2023, 277: 106940.
|
| [79] |
DOODI R, GUNJI B M. Prediction and experimental validation approach to improve performance of novel hybrid bio-inspired 3D printed lattice structures using artificial neural networks[J]. Scientific Reports, 2023, 13(1): 7763. DOI: 10.1038/s41598-023-33935-0
|
| [80] |
WU Y, FANG J, WU C, et al. Additively manufactured materials and structures: a state-of-the-art review on their mechanical characteristics and energy absorption[J]. International Journal of Mechanical Sciences, 2023, 246: 108102.
|
| [81] |
GARAIGORDOBIL A, ANSOLA R, SANTAMARÍA J, et al. A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing[J]. Structural and Multidisciplinary Optimization, 2018, 58: 17-2003. DOI: 10.1007/s00158-018-2000-9
|
| [82] |
DWIVEDI K, JOSHI S, NAIR R, et al. Optimizing 3D printed diamond lattice structure and investigating the influence of process parameters on their mechanical integrity using nature-inspired machine learning algorithms[J]. Materials Today Communications, 2024, 38: 108233. DOI: 10.1016/j.mtcomm.2024.108233
|
| [83] |
JIN Z, ZHANG Z, DEMIR K, et al. Machine learning for advanced additive manufacturing[J]. Matter, 2020, 3(5): 1541-1556. DOI: 10.1016/j.matt.2020.08.023
|
| [84] |
GLAESENER R N, KUMAR S, LESTRINGANT C, et al. Predicting the influence of geometric imperfections on the mechanical response of 2D and 3D periodic trusses[J]. Acta Materialia, 2023, 254: 118918.
|
| [85] |
ZHANG Y, ZHANG Z, FU K, et al. Adaptive defect detection for 3D printed lattice structures based on improved faster R-CNN[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-9.
|
| [86] |
HU E, SEETOH I P, LAI C Q. Machine learning assisted investigation of defect influence on the mechanical properties of additively manufactured architected materials[J]. International Journal of Mechanical Sciences, 2022, 221: 107190. DOI: 10.1016/j.ijmecsci.2022.107190
|
| [87] |
BOCK F E, AYDIN R C, CYRON C J, et al. A review of the application of machine learning and data mining approaches in continuum materials mechanics[J]. Frontiers in Materials, 2019, 6: 110. DOI: 10.3389/fmats.2019.00110
|
| [88] |
MA C, ZHANG Z, LUCE B, et al. Accelerated design and characterization of non-uniform cellular materials via a machine-learning-based framework[J]. Computational Materials, 2020, 6(1): 40.
|
| [89] |
MANNODI-KANAKKITHODI A, PILANIA G, HUAN T D, et al. Machine learning strategy for accelerated design of polymer dielectrics[J]. Scientific Reports, 2016, 6(1): 1-10. DOI: 10.1038/s41598-016-0001-8
|
| [90] |
RACCUGLIA P, ELBERT K C, ADLER P D, et al. Machine-learning-assisted materials discovery using failed experiments[J]. Nature, 2016, 533(7601): 73-76. DOI: 10.1038/nature17439
|
| [91] |
JIE J, HU Z, QIAN G, et al. Discovering unusual structures from exception using big data and machine learning techniques[J]. Science Bulletin, 2019, 64(9): 612-616. DOI: 10.1016/j.scib.2019.04.015
|
| [92] |
LOOKMAN T, BALACHANDRAN PV, XUE D, et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design[J]. Computational Materials, 2019, 5(1): 21. DOI: 10.1038/s41524-019-0153-8
|
| [93] |
RAFIEI MH, KHUSHEFATI W H, DEMIRBOGA R, et al. Neural network, machine learning, and evolutionary approaches for concrete material characterization[J]. ACI Materials Journal, 2016, 113(6): 9979-10015.
|
| [94] |
LIU R, KUMAR A, CHEN Z, et al. A predictive machine learning approach for microstructure optimization and materials design[J]. Scientific Reports, 2015, 5(1): 11551. DOI: 10.1038/srep11551
|
| [95] |
WARD L, AGRAWAL A, CHOUDHARY A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials[J]. Computational Materials, 2016, 2(1): 1-7.
|
| [96] |
BASSMAN-OFDELIE L, RAJAK P, KALIA R K, et al. Active learning for accelerated design of layered materials[J]. Computational Materials, 2018, 4(1): 74. DOI: 10.1038/s41524-018-0129-0
|
| [97] |
GU GX, CHEN CT, BUEHLER MJ. De novo composite design based on machine learning algorithm[J]. Extreme Mechanics Letters, 2018, 18: 19-28. DOI: 10.1016/j.eml.2017.10.001
|
| [98] |
RAFIEI M H, KHUSHEFATI W H, DEMIRBOGA R, et al. Novel approach for concrete mixture design using neural dynamics model and virtual lab concept[J]. ACI Materials Journal, 2017, 114(1).
|
| [99] |
BESSA M A, PELLEGRINO S. Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization[J]. International Journal of Solids and Structures, 2018, 139: 174-188.
|
| [100] |
BESSA M A, BOSTANABAD R, LIU Z, et al. A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality[J]. Computer Methods in Applied Mechanics and Engineering, 2017, 320: 633-667.
|
| [101] |
CHEN C T, GU G X. Effect of constituent materials on composite performance: Exploring design strategies via machine learning[J]. Advanced Theory and Simulations, 2019, 2(6): 1900056. DOI: 10.1002/adts.201900056
|
| [102] |
HANAKATA P Z, CUBUK E D, CAMPBELL D K, et al. Accelerated search and design of stretchable graphene kirigami using machine learning[J]. Physical Review Letters, 2018, 121(25): 255304. DOI: 10.1103/PhysRevLett.121.255304
|
| [103] |
GU G X, CHEN C T, RICHMOND D J, et al. Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment[J]. Materials Horizons, 2018, 5(5): 939-945. DOI: 10.1039/C8MH00653A
|
| [104] |
XU H, LIU R, CHOUDHARY A, et al. A machine learning-based design representation method for designing heterogeneous microstructures[J]. Journal of Mechanical Design, 2015, 137(5): 051403. DOI: 10.1115/1.4029768
|
| [105] |
MARCO M, BELDA R, MIGUELEZ M H, et al. Numerical analysis of mechanical behaviour of lattice and porous structures[J]. Composite Structures, 2021, 261: 113292. DOI: 10.1016/j.compstruct.2020.113292
|
| [106] |
SUR A, DARVEKAR S, SHAH M. Recent advancements of micro-lattice structures: Application, manufacturing methods, mechanical properties, topologies and challenges[J]. Arabian Journal for Science and Engineering, 2021, 46(12): 11587-11600.
|
| [107] |
MOZAFFAR M, LIAO S, XIE X, et al. Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives[J]. Journal of Materials Processing Technology, 2022, 302: 117485. DOI: 10.1016/j.jmatprotec.2021.117485
|
| [108] |
KULAGIN R, BEYGELZIMER Y, ESTRIN Y, et al. Architectured lattice materials with tunable anisotropy: Design and analysis of the material property space with the aid of machine learning[J]. Advanced Engineering Materials, 2020, 22(12): 2001069. DOI: 10.1002/adem.202001069
|
| [109] |
ZHANG C, WANG B, ZHU H, et al. Structure genome based machine learning method for woven lattice structures[J]. International Journal of Mechanical Sciences, 2023, 245: 108134. DOI: 10.1016/j.ijmecsci.2023.108134
|
| [110] |
SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.
|
| [111] |
ZOPH B, GHIASI G, LIN T Y, et al. Rethinking pre-training and self-training[J]. Advances in Neural Information Processing Systems, 2020, 33: 3833-3845.
|
| [112] |
LIU W, WANG Z, LIU X, et al. A survey of deep neural network architectures and their applications[J]. Neurocomputing, 2017, 234: 11-26. DOI: 10.1016/j.neucom.2016.12.038
|
| [113] |
HASSANIN H, ALKENDI Y, ELSAYED M, et al. Controlling the properties of additively manufactured cellular structures using machine learning approaches[J]. Advanced Engineering Materials, 2020, 22(3): 1901338.
|
| [114] |
HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
|
| [115] |
IBRAHIMI S, D ANDREA L, GASTALDI D, et al. Machine learning approaches for the design of biomechanically compatible bone tissue engineering scaffolds[J]. Computer Methods in Applied Mechanics and Engineering, 2024, 423: 116842. DOI: 10.1016/j.cma.2024.116842
|
| [116] |
NAIR V, HINTON G E. Rectified linear units improve restricted Boltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning, 2010: 807-814.
|
| [117] |
FELSCH G, GHAVIDELNIYA N, SCHWARZ D, et al. Controlling auxeticity in curved-beam metamaterials via a deep generative model[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 410: 116032. DOI: 10.1016/j.cma.2023.116032
|
| [118] |
HU B, ZOU W, WU W, et al. Enhanced mechanical and sintering properties of MgO-TiO2 ceramic composite via digital light processing[J]. Journal of the European Ceramic Society, 2022, 42(4): 1694-1702. DOI: 10.1016/j.jeurceramsoc.2021.12.016
|
| [119] |
HU B, WANG Z J, DU C, et al. Multi-objective Bayesian optimization accelerated design of TPMS structures[J]. International Journal of Mechanical Sciences, 2023, 244: 108085.
|
| [120] |
ZHENG X, CHEN TT, JIANG X, et al. Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices[J]. Science and Technology of Advanced Materials, 2023, 24(1): 2157682. DOI: 10.1080/14686996.2022.2157682
|
| [121] |
LIU W, ZHANG Y, LYU Y, et al. Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm[J]. Frontiers in Bioengineering and Biotechnology, 2023, 11.
|
| [122] |
WU C, WAN B, ENTEZARI A, et al. Machine learning-based design for additive manufacturing in biomedical engineering[J]. International Journal of Mechanical Sciences, 2024, 266: 108828. DOI: 10.1016/j.ijmecsci.2023.108828
|
| [123] |
CHEN L, WEI L, SU X, et al. Preparation and characterization of biomimetic functional scaffold with gradient structure for osteochondral defect repair[J]. Bioengineering, 2023, 10(2): 213. DOI: 10.3390/bioengineering10020213
|
| [124] |
DENG Z L, PAN M Z, HUA S B, et al. Mechanical and degradation properties of triply periodic minimal surface (TPMS) hydroxyapatite & akermanite scaffolds with functional gradient structure[J]. Ceramics International, 2023, 49(12): 20808-20816.
|
| [125] |
WANG J, CHEN WW, DA D, et al. IH-GAN: A conditional generative model for implicit surface-based inverse design of cellular structures[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 396: 115060. DOI: 10.1016/j.cma.2022.115060
|
| [126] |
YU G, XIAO L, SONG W. Deep learning-based heterogeneous strategy for customizing responses of lattice structures[J]. International Journal of Mechanical Sciences, 2022, 229: 107531. DOI: 10.1016/j.ijmecsci.2022.107531
|
| [127] |
PATEL D, YANG R, WANG J, et al. Deep learning-based inverse design framework for property targeted novel architectured interpenetrating phase composites[J]. Composite Composite Structures, 2023, 312: 116783. DOI: 10.1016/j.compstruct.2023.116783
|
| [128] |
GONGORA A E, SNAPP K L, PANG R, et al. Designing lattices for impact protection using transfer learning[J]. Matter, 2022, 5(9): 2829-2844. DOI: 10.1016/j.matt.2022.06.051
|
| [129] |
DOLD D, VAN EGMOND D A. Differentiable graph-structured models for inverse design of lattice materials[J]. Cell Reports Physical Science, 2023, 4(10).
|
| [130] |
ISANAKA B R, MUKHOPADHYAY T, VARMA R K, et al. On exploiting machine learning for failure pattern driven strength enhancement of honeycomb lattices[J]. Acta Materialia, 2022, 239: 118226. DOI: 10.1016/j.actamat.2022.118226
|
| [131] |
WANG J, PANESAR A. Machine learning based lattice generation method derived from topology optimisation[J]. Additive Manufacturing, 2022, 60: 103238. DOI: 10.1016/j.addma.2022.103238
|
| [132] |
IANG B, WANG Y, NIU H, et al. GNNs for mechanical properties prediction of strut-based lattice structures[J]. International Journal of Mechanical Sciences, 2024: 109082.
|
| [133] |
WU Y, MAO Z, FENG Y. GNNs for mechanical properties prediction of strut-based lattice structures[J]. Composite Structures, 2023, 319: 117136. DOI: 10.1016/j.compstruct.2023.117136
|
| [134] |
MEYER PP, BONATTI C, TANCOGNE-DEJEAN T, et al. Graph-based metamaterials: deep learning of structure-property relations[J]. Materials & Design, 2022, 223: 111175.
|
| [135] |
CHALLAPALLI A, PATEL D, LI G. Novel composite materials for aerospace applications[J]. Materials & Design, 2021, 208: 109937.
|
| [136] |
YU J, SHI X, FENG Y, et al. Inverse machine learning framework for optimizing lightweight metamaterials[J]. Extreme Mechanics Letters, 2023, 65: 102109.
|
| [137] |
SHAHEEN F, VERMA B, ASAFUDDOULA M. Impact of automatic feature extraction in deep learning architecture[C]//Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications, 2016: 1-8.
|
| [138] |
WEI A, XIONG J, YANG W, et al. Deep learning-assisted elastic isotropy identification for architected materials[J]. Extreme Mechanics Letters, 2021, 43: 101173. DOI: 10.1016/j.eml.2021.101173
|
| [139] |
RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]//Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.
|
| [140] |
YANG H, WANG W, LI C, et al. Deep learning-based X-ray computed tomography image reconstruction and prediction of compression behavior of 3D printed lattice structures[J]. Additive Manufacturing, 2022, 54: 102774. DOI: 10.1016/j.addma.2022.102774
|
| [141] |
SIEGKAS P. Generating 3D porous structures using machine learning and additive manufacturing[J]. Materials & Design, 2022, 220: 110858.
|
| [142] |
SHEN C, WANG C, HUANG M, et al. A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning[J]. Journal of Materials Science & Technology, 2021, 93: 191-204.
|
| [143] |
ZHUANG W, WANG E, ZHANG H. Prediction of compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method[J]. Mechanics of Materials, 2023, 182: 104684. DOI: 10.1016/j.mechmat.2023.104684
|
| [144] |
BERMEJILLO BARRERA MD, FRANCO-MARTÍNEZ F, DÍAZ LANTADA A. Artificial intelligence aided design of tissue engineering scaffolds employing virtual tomography and 3D convolutional neural networks[J]. Materials, 2021, 14(18): 5278.
|
| [145] |
ZHOU K, SUN H, ENOS R, et al. Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties[J]. Computational Materials Science, 2021, 197: 110663. DOI: 10.1016/j.commatsci.2021.110663
|
| [146] |
DESPRES N, CYR E, SETOODEH P, et al. Deep learning and design for additive manufacturing: a framework for microlattice architecture[J]. JOM, 2020, 72: 2408-2418. DOI: 10.1007/s11837-020-04131-6
|
| [147] |
LI X, LIU Z, CUI S, et al. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning[J]. Computer Methods in Applied Mechanics and Engineering, 2019, 347: 735-753. DOI: 10.1016/j.cma.2019.01.005
|
| [148] |
WANG Z, DABAJA R, CHEN L, et al. Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design[J]. Scientific Reports, 2023, 13(1): 5414.
|
| [149] |
MENG L, ZHANG W, QUAN D, et al. From topology optimization design to additive manufacturing: Today's success and tomorrow's roadmap[J]. Archives of Computational Methods in Engineering, 2020, 27: 805-830. DOI: 10.1007/s11831-019-09331-1
|
| [150] |
ZHU J H, ZHANG W H, XIA L. Topology optimization in aircraft and aerospace structures design[J]. Archives of Computational Methods in Engineering, 2016, 23: 595-622. DOI: 10.1007/s11831-015-9151-2
|
| [151] |
RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks for solving differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. DOI: 10.1016/j.jcp.2018.10.045
|
| [152] |
MENG Z, QIAN Q, XU M, et al. PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 414: 116172.
|
| [153] |
CHEN L, SHEN M H H. A new topology optimization approach by physics-informed deep learning process[J]. Advances in Science, Technology and Engineering Systems Journal, 2021, 6(4): 233-240. DOI: 10.25046/aj060427
|
| [154] |
LU L, PESTOURIE R, YAO W, et al. Physics-informed neural networks with hard constraints for inverse design[J]. SIAM Journal on Scientific Computing, 2021, 43(6): B1105-B1132. DOI: 10.1137/21M1397908
|
| [155] |
MARGOSSIAN C C. A review of automatic differentiation and its efficient implementation[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019, 9(4): e1305.
|
| [156] |
KUMAR P, SCHMIDLEITHNER C, LARSEN N, et al. Topology optimization and 3D printing of large deformation compliant mechanisms for straining biological tissues[J]. Structural and Multidisciplinary Optimization, 2021, 63: 1351-1366. DOI: 10.1007/s00158-020-02764-4
|
| [157] |
LI Y, ZHU J, WANG F, et al. Shape preserving design of geometrically nonlinear structures using topology optimization[J]. Structural and Multidisciplinary Optimization, 2019, 59: 1033-1051. DOI: 10.1007/s00158-018-2186-x
|
| [158] |
JEONG H, BAI J, BATUWATTA-GAMAGE C P, et al. A physics-informed neural network-based topology optimization (PINNTO) framework for structural optimization[J]. Engineering Structures, 2023, 278: 115484. DOI: 10.1016/j.engstruct.2022.115484
|
| [159] |
TRINH D T, LUONG K A, LEE J. An analysis of functionally graded thin-walled beams using physics-informed neural networks[J]. Engineering Structures, 2024, 301: 117290.
|
| [160] |
RAISSI M, YAZDANI A, Karniadakis G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations[J]. Science, 2020, 367(6481): 1026-1030. DOI: 10.1126/science.aaw4741
|
| [161] |
LU L, MENG X, MAO Z, et al. DeepXDE: A deep learning library for solving differential equations[J]. SIAM review, 2021, 63(1): 208-228. DOI: 10.1137/19M1274067
|
| [162] |
SUN L, GAO H, PAN S, et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 361: 112732. DOI: 10.1016/j.cma.2019.112732
|
| [163] |
HOSSEINI S, FARROKHABADI A, CHRONOPOULOS D. Experimental and numerical analysis of shape memory sinusoidal lattice structure: Optimization through fusing an artificial neural network to a genetic algorithm[J]. Composite Structures, 2023, 323: 117454.
|
| [164] |
GARLAND A P, WHITE B C, JENSEN S C, et al. Pragmatic generative optimization of novel structural lattice metamaterials with machine learning[J]. Materials & Design, 2021, 203: 109632.
|
| [165] |
ROACH D J, ROHSKOPF A, HAMEL C M, et al. Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures[J]. Additive Manufacturing, 2021, 41: 101950. DOI: 10.1016/j.addma.2021.101950
|
| [166] |
WANG Y, LIAN Y, WANG Z, et al. A novel triple periodic minimal surface-like plate lattice and its data-driven optimization method for superior mechanical properties[J]. Applied Mathematics and Mechanics, 2024, 45(2): 217-238. DOI: 10.1007/s10483-024-3079-7
|
| [167] |
LEE S, ZHANG Z, GU G X. Generative machine learning algorithm for lattice structures with superior mechanical properties[J]. Materials Horizons, 2022, 9(3): 952-960.
|
| [168] |
WANG Y, LIU S, WU H, et al. On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm[J]. e-Polymers, 2020, 20(1): 122-132. DOI: 10.1515/epoly-2020-0014
|
| [169] |
LI Z, HOU X, KE Y, et al. Topology optimization with a genetic algorithm for the structural design of composite porous acoustic materials[J]. Applied Acoustics, 2022, 197: 108917. DOI: 10.1016/j.apacoust.2022.108917
|
| [170] |
RAMOS E M, BORGES M R, GIRALDI G A, et al. Prediction of permeability of porous media using optimized convolutional neural networks[J]. Computational Geosciences, 2023, 27(1): 1-34. DOI: 10.1007/s10596-022-10177-z
|
| [171] |
WANG Y, ZENG Q, WANG J, et al. Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 401: 115571.
|
| [172] |
LIU J P, XIA Y. A hybrid intelligent genetic algorithm for truss optimization based on deep neutral network[J]. Swarm and Evolutionary Computation, 2022, 73: 102828.
|
| [173] |
KUMAR S, TEJANI G G, PHOLDEE N, et al. Performance enhancement of meta-heuristics through random mutation and simulated annealing-based selection for concurrent topology and sizing optimization of truss structures[J]. Soft Computing, 2022, 26(12): 5661-5683. DOI: 10.1007/s00500-022-06930-2
|
| [174] |
SUN Y, WANG S, SHEN Y, et al. Boosting ant colony optimization via solution prediction and machine learning[J]. Computers & Operations Research, 2022, 143: 105769.
|
| [175] |
XU Z, HAN G, LIU L, et al. Multi-energy scheduling of an industrial integrated energy system by reinforcement learning-based differential evolution[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(3): 1077-1090. DOI: 10.1109/TGCN.2021.3061789
|
| [176] |
CHUNG Y, HAAS P J, UPFAL E, et al. Unknown examples & machine learning model generalization[J]. arXiv preprint arXiv: 180808294, 2018.
|
| [177] |
MOLNAR C, KÖNIG G, HERBINGER J, et al. General pitfalls of model-agnostic interpretation methods for machine learning models[C]//Proceedings of the International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, 2020: 39-68.
|
| [178] |
PAULSON NH, KUBAL J, WARD L, et al. Feature engineering for machine learning enabled early prediction of battery lifetime[J]. Journal of Power Sources, 2022, 527: 231127. DOI: 10.1016/j.jpowsour.2022.231127
|
| [179] |
DAVOODI S, THANH H V, WOOD D A, et al. Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables[J]. Separation and Purification Technology, 2023, 316: 123807. DOI: 10.1016/j.seppur.2023.123807
|
| [180] |
MATSUDA Y, OOKAWARA S, YASUDA T, et al. Framework for discovering porous materials: structural hybridization and Bayesian optimization of conditional generative adversarial network[J]. Digital Chemical Engineering, 2022, 5: 100058. DOI: 10.1016/j.dche.2022.100058
|
| [181] |
FOKIN M I, NIKITIN V V, DUCHKOV A A. A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging[J]. Journal of Synchrotron Radiation, 2023, 30(5).
|
| [182] |
YASUDA T, OOKAWARA S, YOSHIKAWA S, et al. Machine learning and data-driven characterization framework for porous materials: Permeability prediction and channeling defect detection[J]. Chemical Engineering Journal, 2021, 420: 130069.
|
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