Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm

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

doi:10.1109/IConAC.2016.7604954

Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm

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

^*Corresponding author for this work

Faculty Of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

17 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016
Subtitle of host publication	Tackling the New Challenges in Automation and Computing
Pages	408-414
Number of pages	7
DOIs	https://doi.org/10.1109/IConAC.2016.7604954
Publication status	Published - 20 Oct 2016
Event	22nd International Conference on Automation and Computing, ICAC 2016 - University of Essex, Colchester, United Kingdom Duration: 7 Sept 2016 → 8 Sept 2016 http://www.cacsuk.co.uk/index.php/conferences

Conference

Conference	22nd International Conference on Automation and Computing, ICAC 2016
Abbreviated title	ICAC 2016
Country/Territory	United Kingdom
City	Colchester
Period	7/09/16 → 8/09/16
Internet address	http://www.cacsuk.co.uk/index.php/conferences

Keywords

big data analytics
cluster k-means
design and manufacture
genetic algorithm
industry 4.0
smart design
smart manufacturing

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10.1109/IConAC.2016.7604954

Cite this

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 Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing (pp. 408-414). Article 7604954 https://doi.org/10.1109/IConAC.2016.7604954

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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.",

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Saldivar, AAF, Goh, C, Li, Y, Chen, Y & Yu, H 2016, Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm. in Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing., 7604954, pp. 408-414, 22nd International Conference on Automation and Computing, ICAC 2016, Colchester, United Kingdom, 7/09/16. https://doi.org/10.1109/IConAC.2016.7604954

Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm. / Saldivar, Alfredo Alan Flores; Goh, Cindy; Li, Yun et al.
Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing. 2016. p. 408-414 7604954.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Li, Yun

AU - Chen, Yi

AU - Yu, Hongnian

PY - 2016/10/20

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N2 - 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.

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KW - genetic algorithm

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Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm

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