4.4 Article

An Online Rolling Model for Plate Mill Using Parallel Computation

期刊

ISIJ INTERNATIONAL
卷 57, 期 11, 页码 2042-2048

出版社

IRON STEEL INST JAPAN KEIDANREN KAIKAN
DOI: 10.2355/isijinternational.ISIJINT-2017-299

关键词

plate mill; rolling schedule; draft setup model; flow stress; rolling function; controlled rolling; parallel computing; GPGPU

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A new online rolling model of the draft schedule setup for a plate mill has been developed. This model comprises plate temperature, rolling force function and flow stress calculations and their coupling for the roll separating force estimation. The roll separating force calculation is also used when the work roll gap control is made realising a precise plate thickness control for each rolling pass, which is often referred to as an adaptive control. The temperature model and roll separating force model, as well as its inverse calculation (calculate entry thickness from exit thickness and given roll separating force), are involved in the draft schedule setup calculations. Plate rolling is carried out according to the setup calculation results and thus the product plate quality is largely attributable to the setup calculation preciseness. In this model, a one dimensional finite element model is employed to the temperature calculation that enables a precise temperature control which is necessary for the Controlled Rolling (CR) technology. Another development includes the rolling force function model; a new mathematical model which takes the peening effect into account, derived from the three-dimensional rigid-plastic finite element calculations. Finally, a flow stress model is developed taking into account the metallurgical nature such as work hardening, recrystallization and recovery. The coupling of these models allows to a physical based precise model without unnecessary artificial fitting parameters. In addition, for eliminating the convergence loop, an attempt has been made introducing a multi thread computing using General Purpose computing on Graphic Processing Unit (GPGPU). Thanks to this parallel computing technique, the computational time was remarkably reduced. The model was installed in a process computer and some trial rolling tests were conducted.

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