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

Flores Saldivar, A., Goh, C. S., Li, Y. , Chen, Y. and Yu, H. (2016) Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm. In: 22nd International Conference on Automation and Computing (ICAC), 2016, University of Essex, Colchester, uk, 7-8 Sept 2016, pp. 408-414. ISBN 9781862181328 (doi: 10.1109/IConAC.2016.7604954)

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.

• Item Type: Conference Proceedings
• 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., Goh, C. S., Li, Y., Chen, Y., and Yu, H.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781862181328
• Copyright Holders: Copyright © 2016 IEEE
• First Published: First published in 22nd International Conference on Automation and Computing (ICAC), 2016: 408-414
• Publisher Policy: Reproduced in accordance with the publisher copyright policy