journal article·2021-11

Stochastic fatigue life prediction of Fiber-Reinforced laminated composites by continuum damage Mechanics-based damage plastic model

Peyman Gholami, Mohammad Ali Farsi, Mohammad Ali Kouchakzadeh

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Abstract

Abstract In this paper, the evolution of elastic–plastic damage in the composite laminates under fatigue conditions is modeled. Continuum damage mechanics (CDM) has been coupled with the bridge micromechanics model to estimate the fatigue damage and life for laminated composite structures. Based on the elastic–plastic bridging model, three damage variables are defined. These variables estimate the fiber, matrix, and fiber/matrix damage response at the ply scale. To model the beginning of plastic deformation, a yield function is utilized, and evolution equations of the damage variables are obtained. Then the developed deformation plastic model is calculated. The model parameters are calibrated by experimental data. The proposed model is implemented in Abaqus by a subroutine. The capability of the developed model under multiaxial loading is investigated. The constitutive behavior of composite laminates in different conditions is compared with experimental data. The estimated fatigue life of the CDM-plastic damage approach for different layups is in accordance with the experimental data. Numerical results confirm the plastic deformation model leads to more accurate fatigue life prediction, especially in low‐cycle fatigue. Finally, a stochastic model is applied to predict the fatigue life of composite laminates. In the stochastic approach, fiber volume fraction, critical damage parameters, and mechanical properties are modeled as random variables with normal distribution. The stochastic model emphasizes the importance of variations of mechanical properties.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Composite Structure Analysis and Optimization · Structural Behavior of Reinforced Concrete

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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