A meso–macro model for a damage tolerance analysis of composite structures

2003 ◽  
Vol 59 (1) ◽  
pp. 37-43 ◽  
Author(s):  
A.V. Lombardi
Keyword(s):  
Composite Structures ◽  
Damage Tolerance ◽  
Tolerance Analysis ◽  
Macro Model ◽  
Damage Tolerance Analysis

Inner Damage and External Dent in Composite Structures after Impact – Part I: Experimental Observations

2013 ◽  
Vol 577-578 ◽  
pp. 457-460
Author(s):  
Christine Espinosa ◽  
Miriam Ruiz-Ayuso ◽  
Frédéric Lachaud
Keyword(s):  
Composite Structures ◽  
Mass Velocity ◽  
Damage Tolerance ◽  
Impact Damage ◽  
Experimental Testing ◽  
Tolerance Analysis ◽  
Small Mass ◽  
Numerical Testing ◽  
Damage Tolerance Analysis ◽  
Dent Depth

The damage tolerance methodology is used here to compare impact damage from experimental testing and virtual (numerical) testing. The first part of the study aims to identify links between experimental internal (delaminated area) and external measurable damage (dent depth) for a typical aeronautical T800S/M21e laminate. Effects of the mass/velocity ratios at some level of impact energy are evaluated. It is shown that a big mass generates denser and larger delamination with about the same dent than a small mass, which is a critical case for damage tolerance analysis. A relation between the external dent depth and internal delaminated area is proposed.

Micromechanics-Based Fracture Risk Assessment Using Integrated Probabilistic Damage Tolerance Analysis and Manufacturing Process Models

2016 ◽  
Author(s):  
Michael P. Enright ◽  
R. Craig McClung ◽  
Kwai S. Chan ◽  
John McFarland ◽  
Jonathan P. Moody ◽  
...  
Keyword(s):  
Grain Size ◽  
Gas Turbine ◽  
Manufacturing Process ◽  
Damage Tolerance ◽  
Random Variables ◽  
Software Tool ◽  
Tolerance Analysis ◽  
Turbine Engine ◽  
Damage Tolerance Analysis ◽  
Time Required

Materials engineering and damage tolerance assessment have traditionally been performed as disjoint processes involving repeated tests that can ultimately prolong the time required for certification of new materials. Computational advances have been made both in the prediction of material properties and probabilistic damage tolerance analysis, but have been pursued primarily as independent efforts. Integrated computational materials engineering (ICME) has the potential to significantly reduce the time required for development and insertion of new materials in the gas turbine industry. A manufacturing process software tool called DEFORM™ has been linked with a probabilistic damage tolerance analysis (PDTA) software tool called DARWIN® to form a new capability for ICME of gas turbine engine components. DEFORM simulates rotor manufacturing processes including forging, heat treating, and machining to compute residual stress and strain, track anomaly location, and predict microstructure including grain size and orientation. DARWIN integrates finite element stress analysis results, fracture mechanics models, material anomaly data, probability of anomaly detection, and inspection schedules to compute the probability of fracture of a gas turbine engine rotor as a function of operating cycles. Previous papers have focused on probabilistic modeling of residual stresses in DARWIN based on manufacturing process training data from DEFORM. This paper describes recent efforts to extend the probabilistic link between DEFORM and DARWIN to enable modeling of residual strain, average grain size, and ALA (unrecrystalized) grain size as random variables. Gaussian Process modeling is used to estimate the relationship among model responses and material processing parameters. These random variables are applied to microstructure-based fatigue crack nucleation and growth models for use in probabilistic risk assessments. The integrated DARWIN-DEFORM capability is demonstrated for a representative engine disk model which illustrates the influences of manufacturing-induced random variables on component fracture risk. The results provide critical insight regarding the potential benefits of integrating probabilistic computational material processing models with probabilistic damage tolerance-based risk assessment.

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