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Digitized by Kansas Correctional Industries

Digitized by Kansas Correctional Industries

In this thesis, an efficient rotation-free (RF) triangle is proposed and applied to drape/cloth simulations in which the cloth often under large displacements and rotations. The RF model is a class of thin plate/shell computational models possessing only 3 translational degrees of freedom per director whilst their domains of influence are larger than their domains of integration. An important advantage of RF models is that they do not use rotational degrees of freedom and, thus, are not plagued by the complication in finite rotations. Among the quadrilateral and triangular RF models, the latter possesses no practical restriction on the nodal distribution and appears to be a good candidate for drape/cloth simulations. The geometrical linear formulation of the RF model is firstly considered. For straight beams and plates, the curvature is directly obtained through a complete quadratic interpolation of the transverse deflection. For linear curved beams and shells, the curvature change is again derived by the interpolation and the transverse deflection is through projection. The linear RF model is then extended to the geometrical nonlinear analyses by using the corotational framework as well as the small strain and small curvature assumptions. For the RF straight beam and plate, constant tangential bending stiffness matrices which do not need to be updated during the iterative solution process are derived. For the RF curved beam and shell, the bending energies and bending internal forces become a bit complicated. However, the tangential bending stiffness matrices can still be approximated by using the constant matrices as if they are initially straight/flat. The constant approximation exhibits negligible adverse effect on the convergence. Comparing with other exiting RF models, the present RF triangle is simple and physical yet its accuracy is competitive. In its application to static drape simulations, realistic drape configurations with obvious folds are predicted. The RF beam is extended to consider static and dynamic analyses of cable structures. Under the same nodal distributions, the present RF model can tolerate larger load increment and time step in static and explicit dynamic analyses, respectively, with respect to the two-node C0beam finite element model. For virtual sewing and dynamic cloth simulations, an integrated system is developed by synergizing the RF triangle, explicit time integration, adaptive remeshing, collision handling, human body modeling, sewing forces and a supplementary bending energy to suppress the non-physical sharp fold formation. The predicted steady-state configurations of the garments after sewing appear to be realistic and agree with our daily perception. The predictions for cloth dynamic deformations on human body model also look realistic and natural. This thesis proposes a simple and efficient rotation-free triangle which is especially suitable for the problems involving large displacements and rotations. Its application in drape/cloth simulations and integration of various techniques in cloth simulations are explored. The present study is of significance in cloth simulations. / published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

This research develops three efficient textile flaw detection methods to facilitate automated textile inspection for the textile-related industries. Their novelty lies in detecting flaws with knowledge directly extracted from textile images, unlike existing methods which detect flaws with empirically specified texture features. The first two methods treat textile flaw detection as a texture classification problem, and consider that defect-free images of a textile fabric normally possess common latent images, called basis-images. The inner product of a basis-image and an image acquired from this fabric is a feature value of this fabric image. As the defect-free images are similar, their feature values gather in a cluster, whose boundary can be determined by using the feature values of known defect-free images. A fabric image is considered defect-free, if its feature values lie within this boundary. These methods extract the basis-images from known defect-free images in a training process, and require less consideration than existing methods on the degree of matching of a textile to the texture features specified for the textile. One method uses matrix singular value decomposition (SVD) to extract these basis-images containing the spatial relationship of pixels in rows or in columns. The alternative method uses tensor decomposition to find the relationship of pixels in both rows and columns within each training image and the common relationship of these training images. Tensor decomposition is found to be superior to matrix SVD in finding the basis-images needed to represent these defect-free images, because extracting and decomposing the tri-lateral relationship usually generates better basis-images. The third method solves the textile flaw detection problem by means of texture segmentation, and is suitable for online detection because it does not require texture features specified by experience or found from known defect-free images. The method detects the presence of flaws by using the contrast between regions in the feature images of a textile image. These feature images are the output of a filter bank consisting of Gabor filters with scales and rotations. This method selects the feature image with maximal image contrast, and partitions this image into regions with morphological watershed transform to facilitate faster searching of defect-free regions and to remove isolated pixels with exceptional feature values. Regions with no flaws have similar statistics, e.g. similar means. Regions with significantly dissimilar statistics may contain flaws and are removed iteratively from the set which initially contains all regions. Removing regions uses the thresholds determined by using Neyman-Pearson criterion and updated along with the remaining regions in the set. This procedure continues until the set only contains defect-free regions. The occurrence of the removed regions indicates the presence of flaws whose extents are decided by pixel classification using the thresholds derived from the defect-free regions. A prototype textile inspection system is built to demonstrate the automatic textile inspection process. The developed methods are proved reliable and effective by testing them with a variety of defective textile images. These methods also have several advantages, e.g. less empirical knowledge of textiles is needed for selecting texture features. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

No description available.

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