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Abstract

A series of thermomechanical workings such as temperature-controlled torsional strains are applied to a specimen of steel at strain and temperature levels and interpass times selected to simulate rolling mill conditions. The measured stress values are compared with the temperatures of the steel during the working periods during which the respective values were obtained. Thermomechanical working schedules are repeated at selected varying starting and terminating temperatures thereby to obtain a series of possible rolling schedules. These simulations are selected so that a varying number of reduction passes in the sequence occur at steel temperatures below temperature A.sub.r3. The value of a selected parameter of the worked steel, e.g. yield strength, is measured at ambient temperature. From the rolling mill analogue of possible rolling schedule simulations, an optimized rolling schedule is selected which will predictably impart to the steel a value of the selected parameter falling within a predetermined range. Linear regression analysis is applied to empirically obtained rolling mill data to derive one or more linear relationships between a selected property (e.g. yield strength) of the steel and rolling mill parameters thereby to permit selection of an optimum rolling schedule suitable to obtain a preselected value of the selected property of the steel.

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