Research On Layout Algorithms Of Irregular Leather Cloth

It has both practical and theoretical value to research on layout algorithms of two-dimensional irregular leather garment. On the one hand, with the development of the global economy and the growing demand for resources, it has become increasingly important to improve material utilization and economical benefit. For now, however, many leather companies are still using manual layout, that leads to spend a lots of time, low production efficiency and high costs, affecting the competitiveness of enterprises and production schedule. Computer automatic nesting technology can improve the cutting efficiency, reduce labor costs, effective conservation of resources and accelerate enterprises innovation and upgrading. It is of high economic value to enhance the economic efficiency of enterprises. On the other hand, two-dimensional irregular packing problem is NP-complete problem, it is difficult for people to find the optimal solution within acceptable time. And due to different application scenarios have different needs and specific constraints, there are still no general solving methods to two-dimensional irregular nesting problem.The paper is a study of layout problem of two-dimensional irregular leather garment. According to irregularity of leather and garment, this paper uses the expansion strategy of central placing and the positioning strategy based on no-fit polygon, then uses two methods, one is adaptive genetic-simulated annealing algorithm based on adaptive probability, the other is the layout algorithm based on particle swarm optimization, to optimize the layout sequence, and does a comparative analysis of the effects of the two algorithms. The main work is as follows:1. According to the characteristics of irregular animal leather and cloth, do a pretreatment for irregular cloth. In this paper, geometric graphical representation is used for leather and cloth. These two-dimensional graphics are made up a variety of geometric elements, such as lines, polylines, circles, arcs, b-spline. The number of points of these graphics is usually very large, and the shape is complicated. So I simplify and disperse the representation of leather, extract the key points and compute the convex hull. Experiments show that simplifying graph and compute convex hull will reduce the precision of graphic, but it increases the speed of the layout and has no effect on layout results at the same time.2. According to the characteristics of nesting problem of two-dimensional irregular cloth, leather is partitioned, cloth is graded, expansion strategy of central placing and the positioning strategy based on no-fit polygon are used. The paper arranges pieces with high priority in the center of leather which is called quality district, then arrange the pieces with low priority in the leather outer ring zone which is called poor district, and improve the method of no-fit polygon to locate pieces. Experiments show that zoning classification and expansion strategy of central placing can improve the utilization of leather; localization based on no-fit polygon can reduce the times of collision detection, and improve the speed and efficiency of nesting.3. In this paper, genetic-simulated annealing algorithm based on adaptive probability and particle swarm optimization are designed and implemented to optimize the layout sequence, and comparative analysis of the effects of the two algorithms is done. Experiments show that the two algorithms each have advantages, we can choose the optimum algorithm according to different applications.4. The paper has designed and implemented a leather digital nesting system to implement the above mentioned strategies and methods to validate the feasibility and practicality, and realize the optimal layout.Relative to the existing algorithms, this paper proposed the layout algorithms of irregular leather garment are more focused, efficiency and utilization has else been improved. However, the improvement of the no-fit polygon calculation and optimized layout algorithm is the focus of the next stage of work.