Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

Chen, Zhangxin; Huang, Tianyu; Shao, Yimin; Li, Yang; Xu, Hongyi; Avery, Katherine; Zeng, Deliang; Chen, Wei; Su, Xuming

doi:10.1016/j.compstruct.2017.12.039

Cited by 58 publications

(18 citation statements)

References 44 publications

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“…Other models to predict tensile response of TBDCs have also been presented in the literature [15,[22][23][24][25][26][27][28]; these models focus either on unidirectional [15,27] or randomly-oriented [22][23][24][25][26]28] composites using either analytical (and therefore efficient) or FE based (and therefore less efficient) formulations. However, some of these models [23,24,26] neglect the effect of delamination between the tows and are therefore unable to capture the effect of the absolute value of the tow thickness on the mechanical response of the material; others [15,27], have focused only on the effect of the tow aspect-ratio rather than on the effect of the absolute value of thickness.…”

Section: Introductionmentioning

confidence: 99%

Ultra-strong and stiff randomly-oriented discontinuous composites: Closing the gap to quasi-isotropic continuous-fibre laminates

Alves

Carlstedt

Ohlsson

et al. 2020

Composites Part A: Applied Science and Manufacturing

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Section: Introductionmentioning

confidence: 99%

Ultra-strong and stiff randomly-oriented discontinuous composites: Closing the gap to quasi-isotropic continuous-fibre laminates

Alves

Carlstedt

Ohlsson

et al. 2020

Composites Part A: Applied Science and Manufacturing

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“…Flow analysis software was used in this study to analyze the compression mold process of the 3D Timon Compress mold module from Toray, Japan [24]. Flow, packing, fiber, and warping modules are included in this analysis module and the several parameters including the mold temperature, mold pressure, and mold time, among others, can be inputted to simulate the flow pattern, mechanical properties of the molded part, and the fiber densities in the analysis region [25,26]. Also, the pressure-volume-temperature (PVT) characteristics and viscous properties of the charged prepregs can be used to calculate a physical property database [27,28].…”

Section: Compression Molding Analysismentioning

confidence: 99%

Optimization for the prepreg compression molding of notebook computer cover using design of experiment and finite element method

Kim

Lee

et al. 2020

SN Appl. Sci.

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Composite materials can be successfully applied to the manufacturing process in the consumer electronics field and can drastically reduce the weight of the several parts. For this reason, it is important to predict the shape change after carrying out compression molding of composite material and to determine the key parameters of the manufacturing process. In this paper, the compression molding process of a notebook computer cover using composite materials was analyzed by the finite element method. In addition, the signal-to-noise ratio has been calculated for the warpage deformations that occurred on the surface of the notebook computer cover. The design of experiment method was applied to determine the optimal parameters in the process. Levels of the optimal process factors were selected for minimum warpage deformation and to verify the proposed method; a physical notebook computer cover was manufactured using these optimal conditions. Experimental results, including those on warpage deformations, show that the proposed method can be useful in the manufacturing of lightweight notebook computer covers using a compression molding machine.

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“…These are generated by cutting SVEs from random spatial locations in a large fiber network. The probability density functions (pdf) /c 1 (E£) and f (T 1 (crt) are approximated based on the kernel density estimators (10) where cp is the standard normal density function and h E 1 , h (T 1 > 0 are smoothing bandwidth. Thereafter, a transformation from the non-Gaussian fields (i:£(x,w) and CTf(x,w)) to correlated Gaussian random fields (g 1 (x,w) and g 2 (x,w)) with zero mean and unit variance is performed.…”

Section: Characterization and Simulation Of Random Spatial Fields Of Strength And Strain To Failure Based On Stochastic Volume Elements (mentioning

confidence: 99%

“…The aforementioned difficulties associated with predicting the mechanical response of larger structures based on random local material properties has been the topic of extensive research for a wide variety of materials including fiber systems [9][10][11][12][13][14][15][16][17]. In recent works, different methods based on stochastic volume elements (SVEs) [18] have been used in a variety of applications to alleviate the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Constitutive Model of Isotropic Thin Fiber Networks Based on Stochastic Volume Elements

et al. 2019

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Thin fiber networks are widely represented in nature and can be found in man-made materials such as paper and packaging. The strength of such materials is an intricate subject due to inherited randomness and size-dependencies. Direct fiber-level numerical simulations can provide insights into the role of the constitutive components of such networks, their morphology, and arrangements on the strength of the products made of them. However, direct mechanical simulation of randomly generated large and thin fiber networks is characterized by overwhelming computational costs. Herein, a stochastic constitutive model for predicting the random mechanical response of isotropic thin fiber networks of arbitrary size is presented. The model is based on stochastic volume elements (SVEs) with SVE size-specific deterministic and stochastic constitutive law parameters. The randomness in the network is described by the spatial fields of the uniaxial strain and strength to failure, formulated using multivariate kernel functions and approximate univariate probability density functions. The proposed stochastic continuum approach shows good agreement when compared to direct numerical simulation with respect to mechanical response. Furthermore, strain localization patterns matched the one observed in direct simulations, which suggests an accurate prediction of the failure location. This work demonstrates that the proposed stochastic constitutive model can be used to predict the response of random isotropic fiber networks of arbitrary size.

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Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

Cited by 58 publications

References 44 publications

Ultra-strong and stiff randomly-oriented discontinuous composites: Closing the gap to quasi-isotropic continuous-fibre laminates

Ultra-strong and stiff randomly-oriented discontinuous composites: Closing the gap to quasi-isotropic continuous-fibre laminates

Optimization for the prepreg compression molding of notebook computer cover using design of experiment and finite element method

Stochastic Constitutive Model of Isotropic Thin Fiber Networks Based on Stochastic Volume Elements

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