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

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.

Citation

Saldivar, A. A. F., Goh, C., Li, Y., Chen, Y., & Yu, H. (2016). Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm. In 2016 22nd International Conference on Automation and Computing (ICAC). https://doi.org/10.1109/iconac.2016.7604954

• Conference Name: 2016 22nd International Conference on Automation and Computing (ICAC)
• Conference Location: Colchester, United Kingdom
• 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