Characterisation of time-dependent, statistical failure of cellulose fibre networks

Mattsson, Amanda; Uesaka, Tetsu

doi:10.1007/s10570-018-1776-5

Cited by 7 publications

(20 citation statements)

References 25 publications

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“…From Coleman's distribution for lifetime, G ( t ), expressed for a general loading history f ( t ), 14 the specific distribution for the case of a constant load f ( t ) = f 0 is 13

G ((), t) = 1 - e^{- {(\frac{f_{0}}{S_{C}})}^{ρβ} t^{β}} .

…”

Section: Methodsmentioning

confidence: 99%

“…One formulation is when the random variable is defined as a ‘time‐to‐failure’ parameter and can be considered as the standard Weibull formulation for reliability analysis purposes 8,9 . The other formulation is taken from a study by Mattsson and Uesaka 13 . In a series of publications, they investigated the applicability of a lifetime distribution model presented by Coleman to the time dependence of mechanical breakdown of fibres 14 .…”

Section: Introductionmentioning

confidence: 99%

“…In a series of publications, they investigated the applicability of a lifetime distribution model presented by Coleman to the time dependence of mechanical breakdown of fibres 14 . Mattsson and Uesaka studied the model both numerically and experimentally and found it applicable to fibre network systems and, to a good approximation, Weibull distributed for large enough networks 13,15,16 . They also identified and promoted three parameters, originating from the Coleman model, to study time‐dependent statistical failure for characterization of a material 17 .…”

Section: Introductionmentioning

confidence: 99%

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Predicting creep lifetime performance in edgewise compression of containerboards and for stacked corrugated board boxes

Holmvall¹

2021

Packag Technol Sci

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Predicting how long time a corrugated board box can be stored with a constant load stacked on top of it without failing is an everyday challenge for a box designer. This is a basic step in corrugated board box material design which is usually resolved by utilizing so-called stacking factors. A stacking factor is the ratio between the compression strength of the box and the stacking load. Higher stacking factors are used to accommodate for longer storing times or high relative humidity conditions. Utilizing stacking factors in the way commonly done today can be a good first approximation. However, this method often neglects or overcompensates for the inherent nonnormal distribution statistical behaviour associated with corrugated board box lifetimes. A higher precision in predicting box lifetimes enables material optimization and, thus, a more efficient use of resources. This work presents a convenient way of predicting lifetimes of both containerboards and corrugated board boxes. The approach additionally introduces the concept of reliability into lifetime prediction.The prediction is made directly from material or structural parameters and the applied load. This is accomplished by relating two different parameter sets for the Weibull distribution. It is shown that lifetime is indeed dependent not only on a stacking factor but also on durability and the variations associated with the material or box.

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“…From Coleman's distribution for lifetime, G ( t ), expressed for a general loading history f ( t ), 14 the specific distribution for the case of a constant load f ( t ) = f 0 is 13

G ((), t) = 1 - e^{- {(\frac{f_{0}}{S_{C}})}^{ρβ} t^{β}} .

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting creep lifetime performance in edgewise compression of containerboards and for stacked corrugated board boxes

Holmvall¹

2021

Packag Technol Sci

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show abstract

“…(3) i=l where h(r) is the mean function and sJ w ) are orthogonal random variables with zero mean and unit variance. Explicit expression for S i (w) can be found by multiplying Equation ( 3) by 1J i (r) and integrating over the spatial domain V according to (4) where use is made of the orthogonality property of the set { 1J i (r)} :': 1 .…”

Section: Random Field Representationmentioning

confidence: 99%

“…This is true for all materials but especially pronounced in disordered materials such as thin fiber networks [1]. Such variations can be the cause of unexplained occasional failures that cannot be predicted by deterministic material models [2][3][4]. It is, therefore, crucial to develop a sound stochastic approach in studying mechanical failure of thin fiber networks of arbitrary size.…”

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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Time-dependent statistical failure of fibre networks: Distributions, size scaling, and effects of disorders

Uesaka

2022

Mechanics of Fibrous Networks

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Characterisation of time-dependent, statistical failure of cellulose fibre networks

Cited by 7 publications

References 25 publications

Predicting creep lifetime performance in edgewise compression of containerboards and for stacked corrugated board boxes

Predicting creep lifetime performance in edgewise compression of containerboards and for stacked corrugated board boxes

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

Time-dependent statistical failure of fibre networks: Distributions, size scaling, and effects of disorders

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