This paper reports on the construction of an integrated tool consisting of a neural network and subjoined local approximation technique for application to the sewing process, especially for selecting optimal interlinings for woolen fabrics. A single hidden layer neural network is constructed with five input nodes, ten hidden nodes, and two output nodes. To train the network with a back-propagation learning algorithm, the mechanical parameters used as inputs for the fabrics are tensile energy, bending rigidity, bending hysteresis, shear stiffness, and shear hysteresis, while mechanical parameters used as outputs for the interlinings are bending rigidity and shear stiffness, all of which are measured on the KES-FB system. Even though the back-propagation algorithm has a higher learning accuracy and can be successfully used to select the appropriate interlining, its learning process is too slow and it gets stuck in a local minimum. This research presents a few methods for improving the efficiency of the learning process. The raw data from the KES-FB system are nonlinearly normalized, and input orders are randomized. This proce dure produces a good result, such that the error values of the prediction model are low despite the relatively small data set for training. After training, the optimal interlinings for unknown fabrics can be correctly selected through mapping and the local approximation method.

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Peirce's model is newly studied to predict the structural and mechanical properties of woven fabrics. The study concerns various fabric structures made from different yarns of cotton, wool, and polyester, and weave structures of plain, twill, satin, and mat. Weave structures and fabric relaxation cause changes in the yam diameter and fabric crimp. Therefore, the diameters of warp and weft yams in Peirce's model are newly defined to determine effective yarn diameters. When the modified diameters are used to predict the structural and mechanical properties of the fabric, the predicted values show excellent agreement with the measured ones. The more relaxed the fabrics are, the more applicable is Peirce's model to predicting fabric thickness and tensile behavior.

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