- author = "Miha Kovacic and Bozidar Sarler",
- title = "Soft annealing productivity optimization",
- journal = "RMZ -- Materials and Geoenvironment",
- year = "2011",
- volume = "58",
- number = "1",
- pages = "29--38",
- month = mar,
- keywords = "genetic algorithms, genetic programming, steel, soft annealing, productivity, hardness, modeling jeklo, mehko zarjenje, produktivnost, trdota, genetsko programiranje, modeliranje",
- ISSN = "1408-7073",
-
URL = "
http://www.rmz-mg.com/letniki/rmz58/RMZ58_0029-0038.pdf",
- size = "9 pages",
- abstract = "The optimal thermo-mechanical processing in steel industry is difficult because of the multi-constituent and multiphase character of the commercial steels, variety of multi-constituent the possible processing paths, and plant specific equipment characteristics. This paper 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 3.24percent. 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.",
- abstract = "Izvlecek: Zaradi tezko dolocljivih lastnosti komercialnih jekel, raznolikosti tehnoloskih poti in specificne opreme je optimalno termomehansko procesiranje v jeklarstvu izredno problematicno. V clanku je predstavljena uporaba genetskega pogramiranja z namenom povecati hitrost transportnega traku zarilne peci in posledicno produktivnost zarilne peci in procesa zarjenja samega. Med tipicno proizvodnjo so bili zbrani podatki (222 vzorcev, 24 kvalitet jekla) o hitrosti peci, kemijski sestavi jekla (masni delezi C, Cr, Mo, Ni in V) ter trdota po Brinellu pred mehkim zarjenju in po njem. Na podlagi zbranih podatkov je bil izdelan matematicni model trdote po mehkem zarjenju z metodo genetskega programiranja. Glede na izracunane vplive na trdoto po mehkem zarjenju smo povecali hitrost zarjenja. Po povecanju hitrosti zarjenja se izmerjene trdote materiala ujemajo z izracunanimi v povprecju 3,24-odstotno. Produktivnost mehkega zarjenja se je povecala iz 3,2 m/h na 7 m/h kot posledica umetne inteligence.",
-
notes = "In English
STORE STEEL, d. o. o., Store, Slovenia
University of Nova Gorica, Laboratory for Multiphase Processes, Nova Gorica, Slovenia
Published in Ljubljana, Slovenija http://www.rmz-mg.com/contents.htm",
