Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Flores Saldivar, A. A., Goh, C., Li, Y. , Yu, H. and Chen, Y. (2017) Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments. In: 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA2016), Chengdu, China, 15-17 Dec 2016, pp. 79-86. ISBN 9781509032983 (doi:10.1109/SKIMA.2016.7916201)

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Abstract

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.

Item Type:Conference Proceedings
Keywords:Smart manufacturing, Industry 4.0, smart design, big data analytics, fuzzy clustering, genetic search
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Flores Saldivar, Mr Alfredo and Goh, Dr Cindy Sf and Li, Professor Yun
Authors: Flores Saldivar, A. A., Goh, C., Li, Y., Yu, H., and Chen, Y.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781509032983
Copyright Holders:Copyright © 2016 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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