From the stone ages to modern history, new materials have often been the enablers of revolutionary technologies.[1] For a wide variety of envisioned applications in space exploration, energy-efficient aircraft, and armor, materials must be significantly stronger, stiffer, and lighter than what is currently available. Carbon nanotubes (CNTs) have extremely high strength, [2][3][4][5] very high stiffness, [6,7] low density, good chemical stability, and high thermal and electrical conductivities.[8]These superior properties make CNTs very attractive for many structural applications and technologies. Here we report CNT fibers that are many times stronger and stiffer per weight than the best existing engineering fibers and over twenty times better than other reported CNT fibers. Additionally, our CNT fibers are nonbrittle and tough, making them far superior to existing materials for preventing catastrophic failure. These new CNT fibers will not only make tens of thousands of products stronger, lighter, safer, and more energy efficient, but they will also bring to fruition many envisioned technologies that have been to date unavailable because of material restrictions. Strong, stiff, and lightweight are critical property requirements for materials that are used in the construction of space shuttles, airplanes, and space structures. These properties are assessed by a material's specific strength and specific stiffness, which are defined as the strength or stiffness (Young's modulus) of a material divided by its density.[9] The combination of high strength, high stiffness, and low density affords CNTs with extremely high values for specific strength and specific stiffness. The most effective way to utilize these properties is to assemble CNTs into fibers. However, despite extensive worldwide efforts to date, the specific strength and specific stiffness of CNT fibers that have been reported by various research groups are much lower than currently available commercial fibers. [10][11][12][13][14][15][16][17][18][19][20][21][22] In early studies, researchers attempted to reinforce polymer fibers with short CNTs, but the reinforcement was limited by several issues, including poor dispersion, poor alignment, poor load transfer, and a low CNT volume fraction. [10][11][12][13][14][15] Recently, pure CNT fibers (also called yarns)were reported with and without twisting. [16][17][18][19][20][21][22] For example, Zhang et al. [20] demonstrated that spinning from aligned CNT arrays could significantly improve the strength of CNT fibers by twisting them. However, to date no breakthrough has been reported in the specific strength and specific stiffness of CNT fibers.Here we report CNT fibers with values for specific strength and specific stiffness that are much higher than values reported for any current engineering fibers as well as previously reported CNT fibers. As shown in Figure 1, the specific strength COMMUNICATION 4198

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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.

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 parameters 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.There are many buzz words in apparel manufacturing, such as just-in-time (JIT) and quick response (QR), which reveal the desire of the apparel manufacturing industry to move away from large production runs towards smaller lots of increasingly diverse goods. It is this shift away from large runs that puts a greater demand on the efficiency of the sewing operation.At the center of the sewing operation is the sewing machine, and in general, an experienced operator is required to set up the sewing machine to properly sew each fabric type. As the manufacturing industry moves toward smaller lots with greater product variability, the sewing operation becomes increasingly inefficient due to frequent trial and error alterations of sewing machine parameters to match fabric properties. As a result, the quantity and quality of goods produced is directly related to the skill of the operator. Automation is seen as a means of deskilling the sewing operation and providing a means of achieving new methods of manufacturing such as IIT and QR.To fully automate apparel assembly, the sewing machine must be able to sense and compensate for changing sewing conditions. If the fabric type or number of plies being sewn changes, the sewing machine should detect this change and alter sewing parameters to optimize seam quality, i.e., the sewing machine should adapt.In this paper, we review several topics within the scope of on-line fabric identification [ 1 ], and present new results readily implemented to provide a powerful tool for on-line fabric classification. Fabric IdentificationThere are many means by which information can be obtained concerning fabric properties. Some of the most popular methods include Kawabata, FAST, the Hatra sewability tester, and the L&M sewability tester. Each of these testing methods has something to offer in terms of fabric evaluation. Kawabata testing (KES) is a method of fabric objective hand evaluation. Four machines are used to provide information on fabric tensile and shesring, bending, compressional, and surface pnopaties. The KES properties have been related to sewing performance of some fabrics [ 15].FAST, or fabric assurance by simpk testing. ...

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