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Abstract
The optimal thermomechanical processing in steel industry is difficult because of the multiconstituent and multiphase character of the commercial steels, variety of the possible processing paths, and plant specific equipment characteristics. This article shows successful implementation of the genetic programming approach for increasing the furnace conveyor speed and consequently productivity of the heat treatment furnace in the soft annealing process. The data (222 samples covering 24 different steel grades) on a furnace conveyor speed, chemical composition of steel (weight percent of C, Cr, Mo, Ni, and V) and Brinell hardness, before and after the soft annealing, were collected during daily production. On the basis of the monitored data a mathematical model for the hardness after the soft annealing was developed by genetic programming. According to the modeled influences on the hardness, the higher furnace conveyor speed was attempted in practice. The experimental results of the hardness after the soft annealing with the increased conveyor speed and the predictions of the mathematical model were compared within the agreement of 3.24%. The genetic model was also compared and verified with linear regression model. The productivity of the soft annealing process increased (from the furnace conveyor speed 3.2 m/h to 7 m/h) as a consequence of the used computational intelligence approach.
ACKNOWLEDGMENT
The authors acknowledge Štore Steel company for the permission to publish the present research. The project was funded by Štore Steel company and the Slovenian Research Agency under grant L2-7204.