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:
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1.
It is user-oriented, because it is based on users' judgments concerning the similarity of the global shape of workpieces.
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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:
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1.
Gathering row data on global shape similarity from a group of representative users and modelling the data by fuzzy numbers.
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2.
Developing benchmark classification for various similarity criteria by using fuzzy clustering analysis.
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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.
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Hsu, S.H., Hsia, T.C. & Wu, M.C. A flexible classification method for evaluating the utility of automated workpiece classification system. Int J Adv Manuf Technol 13, 637–648 (1997). https://doi.org/10.1007/BF01350822
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DOI: https://doi.org/10.1007/BF01350822