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Intelligent Self-learning Control of Levelling Processes by Use of Soft Sensor Techniques to Predict Residual Stress and Flatness (iCONTENS)
Intelligent self-learning control of levelling processes by use of soft sensor techniques to predict residual stress and flatness (iCONTENS)V. DiegelmannH. KrambeerRoger LatheF.-N. LobatoJ.-O. PeräM. Bärwolf
其他書名
Final Report
出版Publications Office of the European Union, 2016
ISBN92795900309789279590030
URLhttp://books.google.com.hk/books?id=thKeAQAACAAJ&hl=&source=gbs_api
註釋IConTens project is focused on developing multi-physics process models for different leveller types for automated auto-adaptive set-up and advanced on-line control of flatness and residual stress using intelligent soft sensors predicting residual stresses and flatness. Bending processes around deflection rolls and coilers were considered while developing strategies for minimum total elongation. This project has been carried out in collaboration among four research institutes, BFI, Mefos, AMMR and CSM, and ONP and AST as industrial partners. The partners have modelled the different levelling procedures applying various modelling techniques. The individual model preferences were described. The importance of a well-adapted material hardening law was pointed out. Supplementary the coiling process was taken into account for having impact on the final material property. Residual stress measurements were carried out to identify possible single influencing parameters. The basic proceeding for the realization of a soft-sensor was defined. The gained knowledge resulted into newly developed set-up and control systems applied on industrial plants. Process optimization, quality improvements and increase of productivity could be reached. The gained knowledge was merged to common recommendations regarding the levelling procedure.