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

9523123和这项研究解决了在皮革行业中使用集成的机器视觉系统来优化裁剪和将废料损失降至最低的问题。这是一个下料问题，其中应用机器视觉技术来优化下料，最大限度地减少废料。在制鞋业中，这种操作是从较大的皮革中切割出较小的不规则皮革。基于车间作业计划的输入和皮革零件的价格，采用系统的方法对切割作业进行计划。机器视觉用于获取皮革件和要为其附着皮革件的鞋件的图像，将它们多角化，并将多角化图像存储在单独的图像数据库中。以皮革件、鞋类零件和制造优先级的多边形图像为输入，开发了解决不规则板材异形下料问题的算法。每个皮革的布局信息被存储在布局数据库中，并作为终端上的图形显示被传送给切割操作员。或者，布局信息被转换为数字控制(NC)代码，并被馈送到用于自动切割(皮革的激光或水射切割)的计算机数字控制(CNC)控制器。一般的下料问题在许多行业都有应用，包括建筑、板材、服装、纺织和玻璃。这些行业中的大部分零件形状不规则。因此，在这些行业中，任何提高原材料利用率的方法或算法都可以大幅减少废品损失，使它们更具竞争力。本项目致力于开发这样一个工具。这项研究的结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.