A fuzzy linguistic approach to data quantification and construction of distance measures for the part family formation problem

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Abstract
Most of the techniques related to part family formation require precise numerical data for parts' features. However, in many situations, the parts' features are too ill-defined and are too complex to be susceptible to analysis by traditional discrete methods. In such cases, this information could be best described using phrases in a natural language. Fuzzy set theory has proved to be a viable alternative to break down barriers between fuzzy human thinking and the machines that accept only precise numerical data. This paper suggests an approach based on fuzzy linguistics to quantify parts feature information for part family formation problem which is traditionally addressed through conventional binary coding structures.

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