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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    The international journal of advanced manufacturing technology 13 (1997), S. 637-648 
    ISSN: 1433-3015
    Keywords: Automated workpiece classification ; Benchmark classification ; Flexible classification method ; Group technology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In group technology, workpieces are categorised into families according to their similarity in design or manufacturing attributes. This categorisation can eliminate design duplication and facilitate the production of workpieces. Much effort has been focused on the development of automated workpiece classification systems. However, it is difficult to evaluate the utility of such systems. The objective of this study was to develop a benchmark classification system based on global shape information for use in evaluating the utility of workpiece classification systems. A classification system has a high level of utility if its classification scheme is consistent with users' perceptual judgment of the similarity between workpiece shapes. Hence, in the proposed method, the consistency between a classification system and users' perceptual judgements is used as an index of the utility of the system. The proposed benchmark classification has two salient characteristics: 1. It is user-oriented, because it is based on users' judgments concerning the similarity of the global shape of workpieces. 2. It is flexible, allowing users to adjust the criteria of similarity applied in the automated workpiece classification. The development of this classification consisted of three steps: 1. Gathering row data on global shape similarity from a group of representative users and modelling the data by fuzzy numbers. 2. Developing benchmark classification for various similarity criteria by using fuzzy clustering analysis. 3. Developing indices for evaluating the appropriate number of workpiece categories and homogeneity within each group. The applicability of the benchmark classification system in evaluating the utility of automated workpiece classification systems was examined.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    The international journal of advanced manufacturing technology 11 (1996), S. 325-335 
    ISSN: 1433-3015
    Keywords: Classification ; Group technology ; Neural network ; Prismatic parts ; Rectilinear ; Skeleton
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper presents a neural network approach to the classification of 3D prismatic parts based on their global shape information modelling. In this approach, a 3D part is modelled by the contours of its three projected views, which are approximately represented by three rectilinear polygons. The global shape information of each polygon is modelled by its simplified skeleton, which originally is of a tree structure and can be represented by several vectors by a conversion method. These vectors are the input to a polygon classifier which is constructed on the basis of the back-propagation neural network model. The classification results of polygons can be used to group the 3D prismatic parts into families in a hierarchical manner, by setting different levels of similarity criteria. The proposed method for classifying 3D workpieces can be used to enhance the productivity of design and manufacturing processes. By retrieving and reviewing similar parts from the part families, the designers or process planners could be greatly assisted in performing a new task. That is, they can avoid the reinvention of an existing design and can create a new design by modifying existing ones.
    Type of Medium: Electronic Resource
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