Cold-rolling mills reduce the thickness of an incoming metal strip to produce longer, thinner strip with desired mechanical, dimensional, and metallurgical properties. A primary factor in manufacturing high-quality cold-rolled sheet is accurately predicting the required rolling force. The rolling force directly influences roll-stack deflections, which correlate to the resulting rolled sheet flatness quality. Increasingly high demand for thin and ultra-thin cold-rolled sheet metal gauges, along with the correspondingly larger sensitivity of flatness defects, makes it more important to accurately and rapidly predict the rolling force before the rolling operation begins. Accurate rolling force predictions enable assignment of appropriate pass schedules and flatness mechanism set-points early in the rolling process, thereby improving quality and reducing time and scrap. Cold rolling force predictions have traditionally employed two-dimensional analytical models such as those proposed by Roberts and Bland & Ford. These simplified methods are prone to inaccuracy, however, because of several uncertain, yet influential, model parameters that are difficult to establish deterministically for wide-ranging products. These parameters include, for example, the rolled strip average compressive yield strength, frictional characteristics relating to low and high mill speeds, and the yield strength strain rate dependency. Conventionally, these unknown parameters have been evaluated deterministically by comparing force predictions with industry force data and using a best-fit regression approach.
In this work, Bayesian updating using a probability function is applied to identify joint posterior probability distributions of the uncertain parameters in rolling force models. It is shown that the non-deterministic Bayesian updating approach is particularly useful as new rolling force data becomes available and the models can “learn” from this available production data. The goal is a model that can better predict necessary mill parameters based on accurate probability estimates of the actual rolling force. The rolling force data used in this work for applying Bayesian updating is actual production data of grades 301, 304L (low carbon), and 304 stainless steels, rolled on a 10-inch wide 4-high cold rolling mill. This force data was collected by observing and averaging load cell measurements at steady rolling speeds.
|Commitee:||Condoor, Sridhar, Karunamoorthy, Swami|
|School:||Saint Louis University|
|School Location:||United States -- Missouri|
|Source:||MAI 52/04M(E), Masters Abstracts International|
|Keywords:||Bayesian, Probabilistic, Rolling|
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