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LSTM water prediction for feedforward control of moulding sand compressibility

  • This paper presents a databased approach for improving the precision of the moulding sand compressibility in the moulding sand mixer of a foundry. In this approach, the deviation between the measured and the target compressibility is reduced by controlling the water addition. The complex dynamic behaviour of the process variables and their influence on the water addition is modelled with a long short-term memory (LSTM) network. Another LSTM network as control path simulates the impact of the water addition on the compressibility. Simulation and experimental results with the applied model for water prediction in a feedforward control yield relevant improvements of the moulding sand compressibility.

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Metadaten
Author:Alexander Rose, Alexander Seel, Bennett Luck, Martin GrotjahnORCiDGND
URN:urn:nbn:de:bsz:960-opus4-19276
DOI:https://doi.org/10.25968/opus-1927
DOI original:https://doi.org/10.1016/j.ifacol.2020.12.2782
Parent Title (English):IFAC-PapersOnLine
Document Type:Article
Language:English
Year of Completion:2020
Publishing Institution:Hochschule Hannover
Release Date:2021/05/18
Tag:batch control; feedforward control; industrial production system; intelligent control; neural control; neural network model; prediction methods; target control
Volume:53
Issue:2
First Page:10417
Last Page:10422
Institutes:Fakult├Ąt II - Maschinenbau und Bioverfahrenstechnik
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International