Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments

This source preferred by Hongnian Yu

Authors: Saldivar, A.A.F., Goh, C., Li, Y., Yu, H. and Chen, Y.

Editors: Cang, S. and Wang, Y.

Pages: 79-86

Publisher: SKIMA

ISBN: 9781509032976

DOI: 10.1109/SKIMA.2016.7916201

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Authors: Saldivar, A.A.F., Goh, C., Li, Y., Yu, H. and Chen, Y.

Pages: 79-86

ISBN: 9781509032976

DOI: 10.1109/SKIMA.2016.7916201

© 2016 IEEE. Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.

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