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

Saldivar, Alfredo Alan Flores and Goh, Cindy and Li, Yun and Yu, Hongnian and Chen, Yi; (2017) Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. In: SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications. IEEE, CHN, pp. 79-86. ISBN 9781509032976 (https://doi.org/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.

ORCID iDs

Saldivar, Alfredo Alan Flores, Goh, Cindy, Li, Yun ORCID logoORCID: https://orcid.org/0000-0002-6575-1839, Yu, Hongnian and Chen, Yi;
  • Item type:Book Section
    ID code:65277
    Dates:
    Date
    Event
    1 May 2017
    Published
    15 October 2016
    Accepted
    Notes:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Technology > Manufactures
    Department:Faculty of Engineering
    Depositing user: Pure Administrator
    Date deposited:27 Aug 2018 12:12
    Last modified:17 Nov 2024 01:30
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65277
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