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

This data was imported from Scopus:

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

Journal: 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing

Pages: 408-414

ISBN: 9781862181311

DOI: 10.1109/IConAC.2016.7604954

© 2016 Chinese Automation and Computing Society. 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.

The data on this page was last updated at 04:57 on May 21, 2019.