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Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Saldivar, Alfredo Alan Flores; Goh, Cindy; Li, Yun; Chen, Yi; Yu, Hongnian

Authors

Alfredo Alan Flores Saldivar

Cindy Goh

Yun Li

Yi Chen



Abstract

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0.

Presentation Conference Type Conference Paper (Published)
Conference Name 2016 22nd International Conference on Automation and Computing (ICAC)
Start Date Sep 7, 2016
End Date Sep 8, 2016
Online Publication Date Oct 24, 2016
Publication Date 2016
Deposit Date Jun 22, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title 2016 22nd International Conference on Automation and Computing (ICAC)
DOI https://doi.org/10.1109/iconac.2016.7604954
Public URL http://researchrepository.napier.ac.uk/Output/2881219