Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results f...Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.展开更多
Using the forearm test, the prickle of 26 commercially available light-weight worsted woven wool fabrics and 7 other fiber fabrics were studied under (24±1)℃ temperature and (65±5)% RH conditions. The surfa...Using the forearm test, the prickle of 26 commercially available light-weight worsted woven wool fabrics and 7 other fiber fabrics were studied under (24±1)℃ temperature and (65±5)% RH conditions. The surface fiber diameter of part of the wool fabrics was measured using a microscope. It was found that most of the light-weight worsted woven wool fabrics gave a prickle sensation under the above conditions. The prickle sensation was significantly correlated with the mean fiber diameter of the surface hairiness. It was also found that the prickle of the light-weight worsted woven wool fabrics was significantly correlated with the number of surface fibers which were coarser than 26 μm diameter.展开更多
Wool & silk blended fancy suiting is desinged. Through trial-production with silk sliver and Australian wool top, the spinning technology is investigated, and the relationship of spinning technology, blending rati...Wool & silk blended fancy suiting is desinged. Through trial-production with silk sliver and Australian wool top, the spinning technology is investigated, and the relationship of spinning technology, blending ratio and yam construction is discussed.展开更多
文摘Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
文摘Using the forearm test, the prickle of 26 commercially available light-weight worsted woven wool fabrics and 7 other fiber fabrics were studied under (24±1)℃ temperature and (65±5)% RH conditions. The surface fiber diameter of part of the wool fabrics was measured using a microscope. It was found that most of the light-weight worsted woven wool fabrics gave a prickle sensation under the above conditions. The prickle sensation was significantly correlated with the mean fiber diameter of the surface hairiness. It was also found that the prickle of the light-weight worsted woven wool fabrics was significantly correlated with the number of surface fibers which were coarser than 26 μm diameter.
文摘Wool & silk blended fancy suiting is desinged. Through trial-production with silk sliver and Australian wool top, the spinning technology is investigated, and the relationship of spinning technology, blending ratio and yam construction is discussed.
