Multiaxial fatigue life prediction of polychloroprene rubber (CR) reinforced with tungsten nano-particles based on semi-empirical and machine learning models

Choi, Joeun; Quagliato, Luca; Lee, Seung Ro; Shin, Junghoon; Kim, Naksoo

doi:10.1016/j.ijfatigue.2020.106136

Cited by 32 publications

(13 citation statements)

References 43 publications

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“…where c indicates the penalty factor. According to the Mercer theorem, the sufficient and necessary condition for a function to be an inner product operation in the feature space is Equation (31).…”

Section: Support Vector Machinementioning

confidence: 99%

“…There are also many studies related to machine learning on creep-fatigue life prediction. Joeun Choi et al [31] conduct a series of fatigue experiments to investigate the fatigue properties of hyper-elastic material. They adopted six different machine learning algorithms to predict the fatigue life and the deep neural network exhibits the best, the average error is 14.3%.…”

Section: Introductionmentioning

confidence: 99%

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Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Dong¹,

Liu²,

Liu³

et al. 2021

Materials

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In order to improve the reliability and service life of vehicle and diesel engine, the fatigue life prediction of the piston in a heavy diesel engine was studied by finite element analysis of piston, experiment data of aluminum alloy, fatigue life model based on energy dissipation criteria, and machine learning algorithm. First, the finite element method was used to calculate and analyze the temperature field, thermal stress field, and thermal–mechanical coupling stress field of the piston, and determine the area of heavy thermal and mechanical load that will affect the fatigue life of the piston. Second, based on the results of finite element calculation, the creep–fatigue experiment of 2A80 aluminum alloy was carried out, and the cyclic response characteristics of the material under different loading conditions were obtained. Third, the fatigue life prediction models based on energy dissipation criterion and twin support vector regression are proposed. Then, the accuracy of the two models was verified using experiment data. The results show that the model based on the twin support vector regression is more accurate for predicting the material properties of aluminum alloy. Based on the established life prediction model, the fatigue life of pistons under actual service conditions is predicted. The calculation results show that the minimum fatigue life of the piston under plain condition is 2113.60 h, and the fatigue life under 5000 m altitude condition is 1425.70 h.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Dong¹,

Liu²,

Liu³

et al. 2021

Materials

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show abstract

“…The use of nanomodified rubber in various branches of technology and, particularly, nanomodified automobile tires are given in [10]. Much attention is currently paid to the issues of numerical modeling of deformation processes and determination of the properties of nanomodified polymers and rubbers and structures based on them, under cyclic [11] and static [12] loadings.…”

Section: Literature Reviewmentioning

confidence: 99%

Crack Resistance Parameters of Nano-reinforced Rubber Products in Mechanical Engineering

Gondlyakh

Kolosov

Scherbina

et al. 2021

Lecture Notes in Mechanical Engineering

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This work is devoted to numerical modeling of the destruction and delamination of spatial systems made of nanomodified materials under static loading conditions. Modeling of thin adhesion layers at the microlevel is carried out within the framework of the relations of the refined iterative-analytical theory of deformation of spatial systems. Numerical integration of the resolving equations is performed by the finite element method. Modeling the degradation of the properties of nanocomposites due to the formation in them of various types of nano, micro, and macro damages was carried out based on the method of discrete-virtual propagation of micro and macro defects. The solution of nonlinear boundary value problems is carried out by the finite element method based on the Newton-Kantorovich algorithm, supplemented by a block that implements the iterative-analytical method of variable approximations. A virtual laboratory for determining nanomodified polymer materials' physical, mechanical and strength characteristics has been developed and implemented. A numerical simulation of the process of pulling out a nanotube from a rubber mass is carried out. The analysis of the accumulation of microdefects and the occurrence of macrodamages of delamination in the contact layer "nanotuberubber" is carried out. The results of numerical calculations are in good agreement with experimental data. The numerical modeling results of the change in the stress-strain state of nanomodified rubber specimens are given depending on the change in the coefficient of their reinforcement with carbon nanotubes.

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“…Pandey and Pokharel [39] presented a DL modeling method to predict spatially resolved 3D crystal orientation evolution of polycrystalline materials under uniaxial tensile loading. Herriott and Spear [40] investigated the ability of deep learning models to predict microstructure-sensitive mechanical properties in metal additive manufacturing and Choi et al [41] used artificial intelligence-based methods to investigate the fatigue life of the hyperelastic anisotropic elastomer W-CR material.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

2021

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Hydrogel has a complex network structure with inhomogeneous and random distribution of polymer chains. Much effort has been paid to fully understand the relationship between mesoscopic network structure and macroscopic mechanical properties of hydrogels. In this paper, we develop a deep learning approach to predict the mechanical properties of hydrogels from polymer network structures. First, network structural models of hydrogels are constructed from mesoscopic scale using self-avoiding walk method. The constructed model is similar to the real hydrogel network. Then, two deep learning models are proposed to capture the nonlinear mapping from mesoscopic hydrogel network structural model to its macroscale mechanical property. A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels under uniaxial tension. Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates that the deep learning models are able to capture the internal relationship between complex network structures and mechanical properties. We hope this approach can provide guidance to structural design and material property design of different soft materials.

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Multiaxial fatigue life prediction of polychloroprene rubber (CR) reinforced with tungsten nano-particles based on semi-empirical and machine learning models

Cited by 32 publications

References 43 publications

Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Crack Resistance Parameters of Nano-reinforced Rubber Products in Mechanical Engineering

Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel

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