An Analysis of Improved Multivariate Process Capability Based on Entropy Weight and Fuzzy Matter Element

doi:10.3969/j.issn.1007-7375.2020.05.015

Abstract

Abstract: In solving the difficulty in data collection of multi-variety and small batch products and the interaction between various indicators, the traditional methods are not effective. The multi-process capability index based on principal component analysis will cause information loss. By using the fuzzy matter element method, the process similar conditions are constructed, the similar process selected to turn the small batch problem into large batch processing, and the tolerance coefficient method is applied to convert the original data into relative data. As a result, an improved multivariate process capability index based on entropy weight theory and fuzzy matter elements is proposed. In conclusion, a product is analyzed for example, thus verifying the practicability and effectiveness of the method.

Key words: multi-variety and small batch, fuzzy matter-element, entropy weight theory, multivariate process capability index (MPCIs)

CLC Number:

TH16

ZHAO Jiali, LI Jiang, XU Yuanping. An Analysis of Improved Multivariate Process Capability Based on Entropy Weight and Fuzzy Matter Element[J]. Industrial Engineering Journal, 2020, 23(5): 109-117.

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