How to achieve a realistic computer simulated effect of plain knitted fabric was explored in this article based on analyzing the geometric model of plain knitted fabric loop. In Visual C++ and OpenGL environment, select the circle as cross-section and use cubic B-Spline curve to simulate the yarn path, then the three-dimensional computer simulation of plain knitted fabric is realized in its general condition and it’s shear condition. The simulation results distinctly show the intermeshed loops and transformation effects of knitted structure in three-dimensional space.
Weft knitted pattern design is one of the most important compositions of textile CAD. Traditional pattern design has a higher request on designers, which can not meet the demand on product development. This paper introduced an automatic approach of weft knitted pattern design, put forward a wave filter which is used to process images based on mathematical functions, described its theory and its algorithm in Visual C++ environment, the result is satisfactory. This method provided a new way for weft knitted pattern design.
Priority has been giving to smart garment materials in modern textile clothing industries. The definition of smart garment material can be described as the material which has the sensory capacity to the stimulation of the surroundings or the environmental elements and can make responses accordingly and meanwhile, maintain the typical features and garment functions. Sensory capacity, feedback and response are the three main elements of the smart garment material. Five relative mature and widely used smart materials, including shape memory materials, waterproof and moisture permeable materials, temperature adaptable materials, photo chromic and thermo chromic materials, and electronic materials, were thoroughly reviewed in terms of concept, theory and up-to-date applications. The aim was to give an overview to national garment designers and manufacturers in China and to explore the potential of developing smart garments for the national market.
As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.
In view of the defect and shortage in cutting path automatic optimization of 2D pattern pieces in current garment automatic cutter, a new optimization method of computer is explored. If there is no cutting path optimization implemented by garment automatic cutter before cutting, some problems will be caused, such as too much unless travel and too long processing time. At present, both at home and abroad, the studies on automatic optimization in cutting preprocessing are relatively weak. According to the “segment cutting from left to right” feature of automatic cutter in cutting process, an algorithm which can be summarized as “segment and reducing point” was proposed. This algorithm combined with the solution of shortest path problem, its purpose is to seek for the approximate optimal solution of cutting path. The algorithm implemented through Visual C++ 6.0 programming. Used in production by enterprise shows that the program is simple to operate, and has a high compute speed. Averagely, unless travel in cutting process reduced more than 10%. It proves that the algorithm is feasible and efficient. Using this algorithm achieved the purpose of reducing unless travel, improving cutting efficiency and lowering the cost.
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