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Accelerating Composite Material Qualification Using Statistically

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Accelerating Composite Material Qualification Using Statistically Significant Multi-Scale Progressive Damage Models

 

Composite materials are either orthotropic or anisotropic and inhomogeneous.  In addition, the material properties are sensitive to any variation in constituent material properties and manufacturing process parameters.  Furthermore, cured composite properties are also sensitive to environment (temperature, moisture and other fluids).  Therefore, even the basic lamina-level characterization of a composite material system requires a significant amount mechanical testing.  These tests can be costly, time-consuming and often hinder application of novel composite materials into new designs and forces designers to use legacy materials that have established material allowable databases.  There are a variety of modeling practices currently in use for predicting the strength of composite laminates. These modeling practices seek to interpret and predict the outcome of physical experiments. Improved multi-scale and multi-physics predictive methodologies for damage tolerance of structures are one of the key needs for composite applications. By leveraging extensive predictive capabilities of current finite element software packages and knowledge of the aerospace industry with advanced analytical techniques, they can be used for developing benchmark examples for evaluating strength and variability of mechanical properties.  The primary goal of this research is to use multi-scale progressive damage growth analyses to predict failure strengths of composites and to simulate the entire allowables development process so that one can trade off risk versus the amount of money spent on testing.  This process is intended to accelerate the insertion of advanced novel materials in new designs by enabling expansion of design variables to optimize structural performance and streamlining design changes in order to reduce product development cycle

 

Authors: Waruna P. Seneviratne, John S. Tomblin, and Tharindu Jayaratne

 

Conference: CAMX 2016 – Anaheim

 

SKU/Code: TP16-0061

 

Pages: 15


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