The paper presents a model for damage accumulation and failure of carbon-carbon (C/C) thick laminates used in automotive disc brakes, with 2.5D reinforcement and disordered lamination sequences. The highly non-linear response of laminates in different stress states was acquired through tensile and Double Cantilever Beam tests performed on specimens cut from discs. The paper adopts a novel bi-phasic approach based on Cohesive Zone Model to model delamination, matrix cracking and fibres failure to predict the structural integrity of laminates. Since the non-homogeneous lay-ups required a robust identification tool for the material properties, modern automatic techniques were used, such as genetic optimization and non-parametric Gaussian process regression, to identify the material model parameters both in the elastic and inelastic ranges. Considering all the tests, the qualitive and quantitative correlation that was achieved indicates that the constitutive law adopted adequately represents the physical damage mechanisms of the C/C material in severe load conditions and that the identification methods adopted represent effective tools to calibrate complex material models for the prediction of the strength and damage tolerance of C/C and, in general, composite laminates.

A model for damage and failure of carbon-carbon composites: development and identification through Gaussian process regression

Airoldi A.;Novembre E.;
2023-01-01

Abstract

The paper presents a model for damage accumulation and failure of carbon-carbon (C/C) thick laminates used in automotive disc brakes, with 2.5D reinforcement and disordered lamination sequences. The highly non-linear response of laminates in different stress states was acquired through tensile and Double Cantilever Beam tests performed on specimens cut from discs. The paper adopts a novel bi-phasic approach based on Cohesive Zone Model to model delamination, matrix cracking and fibres failure to predict the structural integrity of laminates. Since the non-homogeneous lay-ups required a robust identification tool for the material properties, modern automatic techniques were used, such as genetic optimization and non-parametric Gaussian process regression, to identify the material model parameters both in the elastic and inelastic ranges. Considering all the tests, the qualitive and quantitative correlation that was achieved indicates that the constitutive law adopted adequately represents the physical damage mechanisms of the C/C material in severe load conditions and that the identification methods adopted represent effective tools to calibrate complex material models for the prediction of the strength and damage tolerance of C/C and, in general, composite laminates.
2023
2.5D carbon-carbon, Composite damage model, Gaussian process regression, Genetic algorithm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1236510
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