Composite Materials in Design Processes

The use of composite materials in the design process allows one to tailer a component’s mechanical properties, thus reducing its overall weight. On the one hand, the possible combinations of matrices, reinforcements, and technologies provides more options to the designer. On the other hand, it increases the fields that need to be investigated in order to obtain all the information requested for a safe design. This Applied Sciences Special Issue, “Composite Materials in Design Processes”, collects recent advances in the design methods for components made of composites and composite material properties at a laminate level or using a multi-scale approach

Metamodel-based uncertainty quantification for the mechanical behavior of braided composites

The main design requirement for any high-performance structure is minimal dead weight. Producing lighter structures for aerospace and automotive industry directly leads to fuel efficiency and, hence, cost reduction. For wind energy, lighter wings allow larger rotor blades and, consequently, better performance. Prosthetic implants for missing body parts and athletic equipment such as rackets and sticks should also be lightweight for augmented functionality. Additional demands depending on the application, can very often be improved fatigue strength and damage tolerance, crashworthiness, temperature and corrosion resistance etc. Fiber-reinforced composite materials lie within the intersection of all the above requirements since they offer competing stiffness and ultimate strength levels at much lower weight than metals, and also high optimization and design potential due to their versatility. Braided composites are a special category with continuous fiber bundles interlaced around a preform. The automated braiding manufacturing process allows simultaneous material-structure assembly, and therefore, high-rate production with minimal material waste. The multi-step material processes and the intrinsic heterogeneity are the basic origins of the observed variability during mechanical characterization and operation of composite end-products. Conservative safety factors are applied during the design process accounting for uncertainties, even though stochastic modeling approaches lead to more rational estimations of structural safety and reliability. Such approaches require statistical modeling of the uncertain parameters which is quite expensive to be performed experimentally. A robust virtual uncertainty quantification framework is presented, able to integrate material and geometric uncertainties of different nature and statistically assess the response variability of braided composites in terms of effective properties. Information-passing multiscale algorithms are employed for high-fidelity predictions of stiffness and strength. In order to bypass the numerical cost of the repeated multiscale model evaluations required for the probabilistic approach, smart and efficient solutions should be applied. Surrogate models are, thus, trained to map manifolds at different scales and eventually substitute the finite element models. The use of machine learning is viable for uncertainty quantification, optimization and reliability applications of textile materials, but not straightforward for failure responses with complex response surfaces. Novel techniques based on variable-fidelity data and hybrid surrogate models are also integrated. Uncertain parameters are classified according to their significance to the corresponding response via variance-based global sensitivity analysis procedures. Quantification of the random properties in terms of mean and variance can be achieved by inverse approaches based on Bayesian inference. All stochastic and machine learning methods included in the framework are non-intrusive and data-driven, to ensure direct extensions towards more load cases and different materials. Moreover, experimental validation of the adopted multiscale models is presented and an application of stochastic recreation of random textile yarn distortions based on computed tomography data is demonstrated

Multi-scale modelling of damage initiation and progression in textile composite

Composite materials play an ever increasing role in the design of modern day aeronautical and automotive structures due to their weight saving potential. Generally progress in constituent material production and composite manufacturing have resulted in lower costs for composite structures, which has made them more attractive for a number of industries, including the aeronautical and automotive industries. However, while sufficiently accurate numerical models exist to model damage initiation and progression in metal structures similar models are not yet available for composite structures. Yet the ability to model damage accurately is an integral part of the design process in both the aeronautical as well as the automotive industry. Due to the more complex microstructure of textile composites compared to metals a numerical model to predict the behaviour of a macrostructure needs to take microstructural effects into account. Multi-scale modelling approaches are uniquely suited to efficiently incorporate not only micro-scale affects but also higher scale affects like tow buckling. Therefore a multi-scale approach to model damage initiation and progression in textile composites based on the finite element method is presented in this thesis. A number of mechanical tests of a benchmark composite are conducted to measure input parameters for the multi-scale approach as well as mechanical behaviour for comparison with model predictions. The multi-scale approach is used to predict the mechanical behaviour of the benchmark composite for two different load cases, pure tension and pure shear. Results for the pure shear load case show significant deviations between predicted and experimentally measured stress-strain curve. For the pure tension load case transverse strain predictions also deviate significantly from the experimental data, stress-strain data in the loading direction however show good agreement between predicted values and experimentally measured data. Whilst further improvements are still required, the approach presented in this thesis provides a solid foundation for designers to predict damage initiation behaviour and progression in textile composites

NASA Tech Briefs, October 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical' Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Ultrasound for Material Characterization and Processing

Ultrasonic waves are nowadays used for multiple purposes including both low-intensity/high frequency and high-intensity/low-frequency ultrasound. Low-intensity ultrasound transmits energy through the medium in order to obtain information about the medium or to convey information through the medium. It is successfully used in non-destructive inspection, ultrasonic dynamic analysis, ultrasonic rheology, ultrasonic spectroscopy of materials, process monitoring, applications in civil engineering, aerospace and geological materials and structures, and in the characterization of biological media. Nowadays, it is an essential tool for assessing metals, plastics, aerospace composites, wood, concrete, and cement. High-intensity ultrasound deliberately affects the propagation medium through the high local temperatures and pressures generated. It is used in industrial processes such as welding, cleaning, emulsification, atomization, etc.; chemical reactions and reactor induced by ultrasonic waves; synthesis of organic and inorganic materials; microstructural effects; heat generation; accelerated material characterization by ultrasonic fatigue testing; food processing; and environmental protection. This book collects eleven papers, one review, and ten research papers with the aim to present recent advances in ultrasonic wave propagation applied for the characterization or the processing of materials. Both fundamental science and applications of ultrasound in the field of material characterization and material processing have been gathered

Photo-Realistic Rendering of Fiber Assemblies

In this thesis we introduce a novel uniform formalism for light scattering from filaments, the Bidirectional Fiber Scattering Distribution Function (BFSDF). Similar to the role of the Bidirectional Surface Scattering Reflectance Distribution Function (BSSRDF) for surfaces, the BFSDF can be seen as a general approach for describing light scattering from filaments. Based on this theoretical foundation, approximations for various levels of abstraction are derived allowing for efficient and accurate rendering of fiber assemblies, such as hair or fur. In this context novel rendering techniques accounting for all prominent effects of local and global illumination are presented. Moreover, physically-based analytical BFSDF models for human hair and other kinds of fibers are derived. Finally, using the model for human hair we make a first step towards image-based BFSDF reconstruction, where optical properties of a single strand are estimated from "synthetic photographs" (renderings) a full hairstyle