The research presented here pertains to the calibration and validation of one-dimensional and radial flow experimental apparatus used to evaluate the permeability of fibermats used in the making of fiber-reinforced composites. Fibers made from glass or carbon are often used as reinforcements in composites. Such composite materials are popular as engineering materials particularly due to their high strength-to-weight ratio. There are several ways to manufacture such composites. One broad class of such methods is known as the liquid composite molding (LCM) processes, which include resin transfer molding (RTM), vacuum-assisted resin transfer molding (VARTM), Seeman composite resin infusion molding process

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Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for Additive Manufacturing (DC-AM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DC-AM provides an efficient capability to more fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.

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The paper describes the general proposal, function and performance of a prognostics system for fatigue crack growth in airframe structures. Prognostic capabilities are important to a superior Structural Health Monitoring System (SHM). An aim of the prognosis is estimation / prediction of a system / subsystem / structure remaining life, i.e. the time at which damage (crack, corrosion, wear, delamination, disbonding, etc.) will result in a failure of the considered element.