1. Modeling of tensile test results for low alloy steels by linear regression and genetic programming taking into account the non-metallic inclusionsMiha 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: 0
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2. Contour maps for simultaneous increase in yield strength and elongation of hot extruded aluminum alloy 6082Iztok Peruš, Goran Kugler, Simon Malej, Milan Terčelj, 2022, izvirni znanstveni članek Opis: In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.
Ključne besede: AA6082, hot extrusion, mechanical properties, yield strength, elongation, artificial neural networks, analysis
Objavljeno v DKUM: 12.03.2025; Ogledov: 0; Prenosov: 0
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3. Initiation and elongation factor co-expression correlates with recurrence and survival in epithelial ovarian cancerMonika Sobočan, Daniela Brunialti, S Sprung, Christoph Schatz, Jure Knez, Rajko Kavalar, Iztok Takač, Johannes Haybaeck, 2022, izvirni znanstveni članek Opis: High grade epithelial ovarian cancer (EOC) represents a diagnostic and therapeutic challenge due to its aggressive features and short recurrence free survival (RFS) after primary treatment. Novel targets to inform our understanding of the EOC carcinogenesis in the translational machinery can provide us with independent prognostic markers and provide drugable targets. We have identified candidate eukaryotic initiation factors (eIF) and eukaryotic elongation factors (eEF) in the translational machinery for differential expression in EOC through in-silico analysis. We present the analysis of 150 ovarian tissue microarray (TMA) samples on the expression of the translational markers eIF2α, eIF2G, eIF5 (eIF5A and eIF5B), eIF6 and eEF1A1. All translational markers were differentially expressed among non-neoplastic ovarian samples and tumour samples (borderline tumours and EOC). In EOC, expression of eIF5A was found to be significantly correlated with recurrence free survival (RFS) and expression of eIF2G and eEF1A1 with overall survival (OS). Expression correlation among factor subunits showed that the correlation of eEF1A1, eIF2G, EIF2α and eIF5A were significantly interconnected. eIF5A was also correlated with eIF5B and eIF6. Our study demonstrates that EOCs have different translational profile compared to benign ovarian tissue and that eIF5A is a central dysregulated factor of the translation machinery.
Ključne besede: epithelial ovarian cancer, initiation and elongation factor, translational markers
Objavljeno v DKUM: 12.12.2024; Ogledov: 0; Prenosov: 6
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