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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29536


    Title: A genetic-based vision system for cross-functional integration in flexible manufacturing: A tutorial and application
    Authors: Chen,JM
    Contributors: 工業管理研究所
    Keywords: AUTOMATED VISUAL INSPECTION;ALGORITHM APPROACH;COMPUTER VISION;NONCONVEX POLYGONS;CAD MODELS;OPTIMIZATION;SEGMENTATION;DESIGN
    Date: 1997
    Issue Date: 2010-06-29 20:27:27 (UTC+8)
    Publisher: 中央大學
    Abstract: Machine vision has the potential to significantly impact both quality and productivity in automated manufacturing, due to its versatility, flexibility, and relative speed. Unfortunately, algorithmic development has not kept pace with advances in vision hardware technology, particularly in the areas of analysis and decision making. In this article, a tutorial is presented that explains how a genetic algorithm can be applied to vision systems for shape analysis and quality assessment. The control parameters for the algorithm are optimized by conducting experiments of Taguchi's approach to parameter design. The main objective behind this algorithm is to explain an application of the vision system that uses upstream design data of machined parts of different types for downstream metrology and quality decision making in the environment of flexible manufacturing. The part types used for demonstration are restricted to planar polygonal profiles generated by projecting 3D objects onto a 2D inspection plane. The input to the system is a set of boundary features of the part being analyzed, and the outputs from the system include the estimators of size, orientation, position, and out-of-profile error of the part. The system can analyze machined parts of different types without modifying software programs and parameter settings, which makes it generic and flexible, and is inherently suitable for on-line implementation in FMS environments.
    Relation: INTERNATIONAL JOURNAL OF FLEXIBLE MANUFACTURING SYSTEMS
    Appears in Collections:[Graduate Institute of Industrial Management] journal & Dissertation

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