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.
Drape Simulation of Woven Fabric by Using the Finite-element Method
In order to understand better the drape phenomenon of fabrics, a non-linear finite-element code was developed and the three-dimensional drape shapes of a woven fabric were simulated by using the code. The woven fabric was assumed to be an elastic material with orthotropic anisotropy. The fabric was considered as a thin flexible plate under the plane stress condition, and the transverse shear strain was included in the formulation. Since a fabric goes through a large displacement and a large rotation during draping, the drape phenomenon of a fabric is considered as a geometric non-linear phenomenon. The finite-element method was thus formulated based on a total Lagrangian approach. Quadrilateral elements with four nodes were used. In order to avoid the shear-locking phenomenon which is commonly observed in thin-plate analysis, a transverse-shear-strain interpolation method was adopted. The mechanical properties of woven fabrics for drape simulations were measured by using the Kawabata instruments. The drap...
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.