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Modeling of tensile test results for low alloy steels by linear regression and genetic programming taking into account the non-metallic inclusions
Miha Kovačič, Uroš Župerl, 2022, izvirni znanstveni članek

Opis: Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main motive for this study was to study the influences of non-metallic inclusions on mechanical properties obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data (472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium and globular oxides), determined based on ASTM E45, several models for tensile strength, yield strength, percentage elongation, and percentage reduction area were obtained using linear regression and genetic programming. Based on modeling results in the period from January 2022 to April 2022, five successively cast batches of 30MnVS6 were produced with a statistically significant reduction of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary to emphasize that there were no statistically significant changes in other monitored parameters.
Ključne besede: mechanical properties, tensile test, tensile strength, yield strength, percentage elongation, percentage reduction area, low alloy steel, modeling, linear regression, genetic programming, industrial study, steel making, optimization
Objavljeno v DKUM: 24.03.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (3,72 MB)
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Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, izvirni znanstveni članek

Opis: Štore 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 °C 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 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % 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 % and 189 %, 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.
Ključne besede: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Objavljeno v DKUM: 25.03.2024; Ogledov: 284; Prenosov: 19
Celotno besedilo (1,19 MB)
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