An Integrated Machine Vision Based System for Solving the Cutting Stock Problem in the Leather Industry

小行星9523123 本研究解决了在皮革工业中使用集成机器视觉系统优化切割和最小化废料损失的问题。这是一个下料问题，其中机器视觉技术被应用于优化切割和最小化废料。在制鞋业中，这种操作涉及从较大的皮革上切割出较小的不规则皮革。基于输入的车间调度和皮革零件的价格，一个系统的方法来规划切割操作。机器视觉用于获取皮革件和皮革件将被附接至其的鞋部件的图像，对它们进行识别，并将识别的图像存储在单独的图像数据库中。利用皮革片、鞋部件的多边形图像和制造优先级作为输入，开发了用于解决从不规则片材上切割不规则形状下料问题的算法。每个皮革片的布局信息存储在布局数据库中，并作为终端上的图形显示传送给切割操作员。或者，将布局信息转换成数控（NC）代码并馈送到计算机数控（CNC）控制器以用于自动切割（皮革的激光或水射流切割）。 一般的下料问题在许多行业都有应用，包括建筑、钣金、服装、纺织和玻璃。这些行业中的大多数零件形状不规则。因此，任何提高这些行业原材料利用率的方法或算法都可以大幅减少废料损失，使其更具竞争力。该项目的重点是开发这样一个工具。这项研究的成果还将有助于这些行业中的一些关键操作实现自动化，从而减少对人力和经验的过度依赖。

9523123 Anand This research addresses the problem of using an integrated machine vision system in the optimization of the cut and minimization of the scrap losses in the leather industry. This is a cutting stock problem in which machine vision technology is applied to optimize the cuts and minimize scrap. The operation involves cutting smaller irregular leather pieces from a larger piece in the shoe industry. Based on input from shop schedule and the prices of leather parts, a systems approach is applied for planning the cutting operations. The machine vision is used in acquiring the image of the leather pieces and the shoe parts for which the leather pieces are to be attached, polygonizing them, and storing the polygonized images in separate image databases. Using the polygonal images of the leather pieces, the shoe parts, and the manufacturing priorities as input, algorithms are developed for solving the irregular shape cutting stock problem from irregular sheets. The layout information for each leather piece is stored in a layout database and communicated to the cutting operator as a graphic display on a terminal. Alternatively, the layout information is converted to a numerical control (NC) code and fed to a computer numerical control (CNC) controller for automated cutting (laser or waterjet cutting of leather). The general cutting stock problem has applications in a number of industries, including construction, sheet metal, apparel, textile, and glass. Most of the parts in these industries are irregular in shape. Therefore, any methodology or algorithm that improves the utilization of raw materials in these industries can substantially reduce scrap losses and make them more competitive. This project is focused at developing such a tool. The outcome of this research will also help automate some of the critical operations in these industries and thus, reduce overdependency on human labor and experience.