Reduction of surface defects by optimization of casting speed using genetic programming: An industrial case study
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- @Article{Kovacic2023501,
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author = "Miha Kovacic and Uros Zuperl and Leo Gusel and
Miran Brezocnik",
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title = "Reduction of surface defects by optimization of
casting speed using genetic programming: An industrial
case study",
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journal = "Advances in Production Engineering \& Management",
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year = "2023",
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volume = "18",
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number = "4",
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pages = "501--511",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Continuous
casting of steel, Surface defects, Automatic control,
Machine learning, Modeling, Optimization, Prediction,
Linear regression",
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ISSN = "1854-6250",
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URL = "https://apem-journal.org/Archives/2023/Abstract-APEM18-4_501-511.html",
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URL = "https://apem-journal.org/Archives/2023/APEM18-4_501-511.pdf",
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DOI = "doi:10.14743/apem2023.4.488",
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size = "11 pages",
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abstract = "Store Steel Ltd. produces more than 200 different
types of steel with a continuous caster installed in
2016. Several defects, mostly related to
thermomechanical behaviour in the mould, originate from
the continuous casting process. The same casting speed
of 1.6 m/min was used for all steel grades. In May
2023, a project was launched to adjust the casting
speed according to the casting temperature. This
adjustment included the steel grades with the highest
number of surface defects and different carbon content:
16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10
degree Centigrade deviation from the prescribed casting
temperature, the speed was changed by 0.02 m/min.
During the 2-month period, the ratio of rolled bars
with detected surface defects (inspected by an
automatic control line) decreased for the mentioned
steel grades. The decreases were from 11.27 percent to
7.93 percent, from 12.73 percent to 4.11 percent, from
16.28 percent to 13.40 percent, and from 25.52 percent
to 16.99 percent for 16MnCrS5, C22, 30MnVS5, and
46MnVS5, respectively. Based on the collected chemical
composition and casting parameters from these two
months, models were obtained using linear regression
and genetic programming. These models predict the ratio
of rolled bars with detected surface defects and the
length of detected surface defects. According to the
modelling results, the ratio of rolled bars with
detected surface defects and the length of detected
surface defects could be minimally reduced by 14
percent and 189 percent, respectively, using casting
speed adjustments. A similar result was achieved from
July to November 2023 by adjusting the casting speed
for the other 27 types of steel. The same was predicted
with the already obtained models. Genetic programming
outperformed linear regression.",
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notes = "STORE STEEL, d.o.o., Research and Development, Store,
Slovenia
http://apem-journal.org CPE, University of Maribor,
Slomskov trg 15, SI 2000 Maribor, Slovenia",
- }
Genetic Programming entries for
Miha Kovacic
Uros Zuperl
Leo Gusel
Miran Brezocnik
Citations