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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 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 languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Automation and Computing, ICAC 2016
Subtitle of host publicationTackling the New Challenges in Automation and Computing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages408-414
Number of pages7
DOIs
Publication statusPublished - 20 Oct 2016
Event22nd International Conference on Automation and Computing, ICAC 2016 - Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016

Conference

Conference22nd International Conference on Automation and Computing, ICAC 2016
CountryUnited Kingdom
CityColchester
Period7/09/168/09/16

Fingerprint

Customization
Clustering algorithms
Clustering Algorithm
Customers
Genetic algorithms
Attribute
Genetic Algorithm
K-means
Industry
Mass Customization
Computational Intelligence
Production Systems
Cloud computing
Cloud Computing
Productivity
Artificial intelligence
Assign
Data analysis
Railroad cars
Prediction

Keywords

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

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). [7604954] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2016.7604954
Saldivar, Alfredo Alan Flores ; Goh, Cindy ; Li, Yun ; Chen, Yi ; Yu, Hongnian. / Identifying smart design attributes for Industry 4.0 customization using a clustering genetic algorithm. Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 408-414
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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, Institute of Electrical and Electronics Engineers Inc., 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; Chen, Yi; Yu, Hongnian.

Proceedings of the 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing. Institute of Electrical and Electronics Engineers Inc., 2016. p. 408-414 7604954.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Saldivar AAF, Goh C, Li Y, Chen Y, Yu H. 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 408-414. 7604954 https://doi.org/10.1109/IConAC.2016.7604954