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J. Lamon - One of the best experts on this subject based on the ideXlab platform.
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Statistical Flaw strength distributions for glass fibres: Correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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Statistical Flaw strength distributions for glass fibres: correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:Abstract The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size.
G. Foray - One of the best experts on this subject based on the ideXlab platform.
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Statistical Flaw strength distributions for glass fibres: Correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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Statistical Flaw strength distributions for glass fibres: correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:Abstract The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size.
Sankaran Mahadevan - One of the best experts on this subject based on the ideXlab platform.
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Inference of equivalent initial Flaw Size under multiple sources of uncertainty
International Journal of Fatigue, 2011Co-Authors: Shankar Sankararaman, You Ling, Chris Shantz, Sankaran MahadevanAbstract:A probabilistic methodology is proposed in this paper to estimate the equivalent initial Flaw Size (EIFS) distribution accounting for various sources of variability, uncertainty and error, for mechanical components with complicated geometry and multi-axial variable amplitude loading conditions. A Bayesian approach is used to calibrate the distribution of EIFS, where the likelihood function is constructed from model-based fatigue crack growth analysis and inspection results. The variability, uncertainties and errors in the above procedures are quantified, and the distribution of EIFS is calibrated by explicitly accounting for the various sources of uncertainty. Three types of uncertainty are considered: (1) natural variability in loading and material properties; (2) data uncertainty, due to crack detection uncertainty, measurement errors, and sparse data; (3) modeling uncertainty and errors during crack growth analysis, numerical approximations, and finite element discretization. A Monte Carlo simulation-based approach is developed for uncertainty quantification in the crack growth analysis and for constructing the likelihood function of EIFS. The proposed methodology is illustrated by a numerical example.
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statistical inference of equivalent initial Flaw Size with complicated structural geometry and multi axial variable amplitude loading
International Journal of Fatigue, 2010Co-Authors: Shankar Sankararaman, You Ling, Sankaran MahadevanAbstract:Abstract This paper presents several efficient statistical inference techniques to calibrate the equivalent initial Flaw Size (EIFS) of fatigue cracks for mechanical components with complicated geometry and multi-axial, variable amplitude loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis, resulting in rapid computation. Experimental data (crack Size after a particular number of loading cycles) and statistical methods are used to calibrate the EIFS. The methods of least squares and maximum likelihood method are extended to evaluate the entire probability distribution of EIFS. Bayesian techniques are also implemented for this purpose. A fast numerical integration technique is developed as an efficient alternative to the expensive Markov Chain Monte Carlo sampling approach in the Bayesian analysis. An application problem of cracking in a cylindrical structure is used to illustrate the proposed methods.
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probabilistic fatigue life prediction using an equivalent initial Flaw Size distribution
International Journal of Fatigue, 2009Co-Authors: Sankaran MahadevanAbstract:A new methodology is proposed in this paper to calculate the equivalent initial Flaw Size (EIFS) distribution. The proposed methodology is based on the Kitagawa–Takahashi diagram. Unlike the commonly used back-extrapolation method for EIFS calculation, the proposed methodology is independent of applied load level and only uses fatigue limit and fatigue crack threshold stress intensity factor. The advantage of the proposed EIFS concept is that it is very efficient in calculating the statistics of EIFS. The developed EIFS methodology is combined with probabilistic crack growth analysis to predict the fatigue life of smooth specimens. Model predictions are compared with experimental observations for various metallic materials.
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An efficient method for equivalent initial Flaw Size (EIFS) calculation
49th AIAA ASME ASCE AHS ASC Structures Structural Dynamics and Materials Conference <br> 16th AIAA ASME AHS Adaptive Structures Conference<br, 2008Co-Authors: Yongming Liu, Sankaran MahadevanAbstract:A new methodology to calculate the equivalent initial Flaw Size (EIFS) distribution is proposed in this paper. The proposed methodology is based on the Kitagawa-Takahashi diagram and only uses fatigue limit and fatigue crack threshold stress intensity factor under constant amplitude load. Unlike the commonly used back-extrapolation method for EIFS calculation, the proposed methodology is independent of applied load level by definition and is very efficient in calculating the statistics of EIFS. The developed EIFS methodology is combined with a probabilistic crack growth analysis to predict the fatigue life of a smooth specimen. Model predictions are compared with experimental observations for various metallic materials.
Mohamed R'mili - One of the best experts on this subject based on the ideXlab platform.
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Statistical Flaw strength distributions for glass fibres: Correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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Statistical Flaw strength distributions for glass fibres: correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:Abstract The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size.
A. Descamps-mandine - One of the best experts on this subject based on the ideXlab platform.
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Statistical Flaw strength distributions for glass fibres: Correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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Statistical Flaw strength distributions for glass fibres: correlation between bundle test and AFM-derived Flaw Size density functions
Acta Materialia, 2012Co-Authors: G. Foray, A. Descamps-mandine, Mohamed R'mili, J. LamonAbstract:Abstract The present paper investigates glass fibre Flaw Size distributions. Two commercial fibre grades (HP and HD) mainly used in cement-based composite reinforcement were studied. Glass fibre fractography is a difficult and time consuming exercise, and thus is seldom carried out. An approach based on tensile tests on multifilament bundles and examination of the fibre surface by atomic force microscopy (AFM) was used. Bundles of more than 500 single filaments each were tested. Thus a statistically significant database of failure data was built up for the HP and HD glass fibres. Gaussian Flaw distributions were derived from the filament tensile strength data or extracted from the AFM images. The two distributions were compared. Defect Sizes computed from raw AFM images agreed reasonably well with those derived from tensile strength data. Finally, the pertinence of a Gaussian distribution was discussed. The alternative Pareto distribution provided a fair approximation when dealing with AFM Flaw Size.