Abstract
Following the first three industrial revolutions, Industry 4.0 (I4) aims at realizing mass customization at a mass production cost. Currently, however, there is a lack of smart analytics tools for achieving such a goal. This paper investigates this issues and then develops a predictive analytics framework integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a self-organizing map (SOM) 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 selection of patterns from big data with SOM helps with clustering and with the selection of optimal attributes. A car customization case study shows that the SOM is able to assign new clusters when growing knowledge of customer needs and wants. The self-organizing tool offers a number of features suitable to smart design that is required in realizing Industry 4.0.
| Original language | English |
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| Title of host publication | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
| Pages | 5317-5324 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 14 Nov 2016 |
| Event | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 http://www.wcci2016.org/ |
Conference
| Conference | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
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| Abbreviated title | IEEE CEC 2016 |
| Country/Territory | Canada |
| City | Vancouver |
| Period | 24/07/16 → 29/07/16 |
| Internet address |
Keywords
- big data analytics
- industry 4.0
- self-organizing map
- smart design
- smart manufacturing