Timothy G. Clapp

Finite-element modeling and control of flexible fabric parts

Software based on nonlinear shell theory can simulate 3D motions related to real fabric-manufacturing processes. This simulation capability advances the technologies necessary for automating the textile and apparel industries.

Buckling of Woven Fabrics Part II: Effect of Weight and Frictional Couple

The effect of fabric weight is incorporated into a theoretical model based on Gros bergs frictional couple theory for describing the buckling behavior of woven fabrics. Numerical solutions for fabric models with free- and fixed-end conditions are studied using various combinations of weight and friction couple factors. The weight factor is defined as the fabric weight per unit length to bending rigidity times the buckling length cubed. The friction couple factor is defined as the moment required to initiate the deformation in the fabric times the bending length divided by the bending rigidity. Results show that in the initial buckling stage, the buckling force increases as the weight factor increases above the force attributed to the friction couple factor alone. The weight factor and friction couple factor are shown to influence the buckling force linearly, and independently for both end conditions in the initial buckling stage. In the range of large deflection (>60%), increasing the friction couple factor continues to increase the buckling force, but the contribution due to the increment of the weight factor is either negative (free-free ends) or negligible ( fixed-fixed ends).

Indirect Measurement of the Moment-Curvature Relationship for Fabrics

An indirect method of experimentally measuring the moment-curvature relationship for fabrics is developed in this study. The new test method involves recording the deformed coordinates of a fabric sample as it is cantilevered under its own weight from a fixed support. By applying least squares polynomial regression and numerical differentiation techniques, the coordinate data along with the values of fabric weight per unit area are used to construct the moment-curvature relationship of the fabric. This method and its associated computer algorithm have been validated by numerical simulations and experimental observations. The nonlinear moment-curvature rela tionship was used to approximate the nonlinear bending stiffness in fabrics. The ad vantage of this method is that the fabric nonlinear bending behavior, which is inherent in most fabrics, can be well represented; this property may not always be obtained from the traditional cantilever beam test.

Buckling of Woven Fabrics Part I: Effect of Fabric Weight

The effect of weight on the buckling behavior of woven fabrics is investigated in this study. Using Timoshenkos elastica theory, the differential equation of the fabric is established, and numerical solutions under three different boundary conditions are obtained. This model explains the buckling behavior of the fabric as efficiently as the model using friction couple theory. In addition, the relationship between the weight factor (the ratio of fabric weight per unit length to bending rigidity times buckling length cubed) of the fabric and the load-deflection characteristics are discussed in detail. As the weight factor increases, the force to initiate buckling increases. For a fabric undergoing a large deflection, the effect of the weight factor w on buckling is strongly dependent on the boundary conditions of the fabric elastica. In many apparel fabrics, the weight factor is significant and cannot be neglected in the model formu lation.

AN ON-LINE FABRIC CLASSIFICATION TECHNIQUE USING A WAVELET-BASED NEURAL NETWORK APPROACH

A sewing system is described that classifies both the fabric type and number of plies encountered during apparel assembly, so that on-line adaptation of the sewing param eters to improve stitch formation and seam quality can occur. Needle penetration forces and presser foot forces are captured and decomposed using the wavelet transform. Salient features extracted using the wavelet transform of the needle penetration forces form the input to an artificial neural network, which classifies the fabric type and number of plies being sewn. A functionally linked wavelet neural network is trained on a moderate number of stitches for five fabrics, and can correctly classify both fabric type and number of plies being sewn with 97.6% accuracy. This network is intended for use with dedicated DSP hardware to classify fabrics on-line and control sewing parameters in real time.

Automated Apparel Processing: Computer Simulation of Fabric Deformation for the Design of Equipment

Introduces and evaluates a finite‐element computer model which predicts the bending behaviour of fabric in contact with a surface, in order to optimize the design of equipment used for automated processing of apparel. Describes how simulations were executed for all combinations of eight fabrics and three contact surfaces, and presents the experimental results obtained for similar conditions and fabrics. Proves the validity of the computer model by comparing the experimental results with those obtained by simulation. Describes how the computer model could be used to choose the optimum diameter of a fabric feeder picking roller.

Buckling of Woven Fabrics Part III: Experimental Validation of Theoretical Models

The buckling behavior of fabrics initially lying on a horizontal surface has been studied in a three-part series. Parts I and II developed theoretical models that included the effects of fabric weight and nonlinear stiffness properties, respectively. Part III is an experimental study designed to validate the theoretical models. A special apparatus was developed to measure the load-deflection curves of a fabric in buckling. Two materials, a plain weave fabric and a cellulose film, were selected to study the effect of fabric weight and nonlinear bending stiffness on the buckling behavior. The ex perimental results compared favorably with the theoretical predictions. The model that included the fabric weight and realistic nonlinear bending stiffness most accurately represented the actual load-deflection behavior. The model and the experimental results showed that the force to initiate buckling increased dramatically as the weight factor or the nonlinearity of the bending stiffness increased.

An XY‐Theta manipulator for flexible fabric part positioning

Fully automating the apparel assembly process will require generic robotic manipulators which can be purchased as a complete unit and adapted to specific applications. A manipulator that can handle and position non‐rigid fabric parts of inexact dimension is described. The XY‐theta provides three directions of fabric positioning. Further, the manipulator can handle different sizes of a variety of styles. The aligning system is composed of three modules‐ vision, positioning and control, which exchange information and control signals via a 32‐bit bus system. This combination of modules and bus system makes the aligning system very flexible which is represented by the ability to switch the type of microcontroller or motors used. Thus, the generic system can be adapted for specific applications.

Development of a felled seam monitoring device

This paper discusses the electrical design of a seam monitoring device for felled seams. Felled seams are commonly found in commercial products, such as apparel garments, parachutes, tenting and geotextiles. The felled seam can be improperly formed during the seaming process, producing defects that may not be detected by visual inspection. The felled seam monitoring device ensures the detection of a faulty felled seam during the seam formation process. The design focuses on the utilization of inexpensive electronic technology and analysis of displacement-based sampling techniques to meet apparel manufacturer’s design specifications.