Solid-mechanics finite element simulations of the draping of fabrics: a sensitivity analysis
Abstract This paper covers numerical investigations of the draping of woven fabrics into a “hat” shape, combining a hemispherical cup with a wide flat rim. A mechanical approach is adopted using finite element analysis (FEA) methodology. In this, the fabric is considered as a solid sheet with mechanical properties and friction properties. In this study, a linear elastic anisotropic material model describes the deformation of fabrics. An explicit dynamic finite element analysis is applied and systematic parametric numerical studies are presented, which incorporate investigations of the effects of numerical parameters, material properties and processing conditions on the draping of fabrics. More specifically, the effects of the following variables and parameters are included: number of elements, number of time increments in the dynamic FEA analysis, punch speed, shear and tensile moduli of fabric, coefficient of friction for all interfaces and level of load on the fabric holder.
A modified Fast Marching Method for defining fiber placement trajectories over meshes
Abstract We study the efficiency of applying the numerical simulation of a moving interface to the problem of defining fiber placement trajectories for composite structures. This new approach, which is based on an adaptation of the Fast Marching Method, presents two advantages. Firstly, it simulates the mechanism of an Automated Fiber Placement machine by producing equidistant fiber courses, without undesirable gaps or overlaps between the successive courses. Secondly, it is able to handle complex surfaces by working directly on a mesh instead of geometric equations. This approach is illustrated through examples from real case studies for straight and curved fiber trajectories.
Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques
When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GLCM technique for feature extraction and by using Wavelet decomposition; HSV images are giving the best results. While other formats also give the correct retrieval but from the accuracy point of view, HSV images are retrieved with better accuracy using both techniques.