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Erschienen in: Journal of Intelligent Manufacturing 4/2016

23.05.2014

The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production

verfasst von: Jia-Bei Yu, Yang Yu, Lin-Na Wang, Ze Yuan, Xu Ji

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2016

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Abstract

Based on the characteristics of ready-mixed concrete enterprises, this paper puts forward that knowledge management (KM) is an effective way to contribute to enterprise production and operation. The knowledge content and relevant models of concrete enterprises are proposed, including advanced enterprise management, decision support for production operation, production and operation cost, and marketing-customer relationship. Afterwards knowledge contents are divided into static, strategic and reasoning knowledge. Besides knowledge unified expression is put forward accordingly. In addition, the KM system for process ready-mixed concrete enterprises management is established to facilitate effective production processing. As part of exploratory study, artificial neural network coupled with genetic algorithm (ANN-GA) as knowledge mining technology is applied in KM system to predict the 28-day compressive strength in concrete enterprises. The results shows that compared to back-propagation artificial neural network, the convergence rate of ANN-GA algorithm has been significantly improved and almost all the relative errors of predicted compressive strength of concrete C30 are within 3 %. It not only confirms the validity of the models, but also proves that ANN-GA algorithm is an effective knowledge mining method applied in concrete industry.

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Metadaten
Titel
The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production
verfasst von
Jia-Bei Yu
Yang Yu
Lin-Na Wang
Ze Yuan
Xu Ji
Publikationsdatum
23.05.2014
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 4/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-014-0923-6

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