Analysis of predictors for modification of alumina inclusions in medium carbon steel
Currently in steelmaking, “inclusions engineering” has occupied a relevant space in terms of improvements in processes and products. This is because these compounds, depending on the chemical nature, morphology, physical state, size and distribution, can harm both the processes and the mechanical pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785421007468 |
| _version_ | 1819103455953813504 |
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| author | Raphael Mariano de Souza Márcia Spelta de Oliveira José Roberto de Oliveira Eduardo Junca Victor Bridi Telles Felipe Fardin Grillo |
| author_facet | Raphael Mariano de Souza Márcia Spelta de Oliveira José Roberto de Oliveira Eduardo Junca Victor Bridi Telles Felipe Fardin Grillo |
| author_sort | Raphael Mariano de Souza |
| collection | DOAJ |
| description | Currently in steelmaking, “inclusions engineering” has occupied a relevant space in terms of improvements in processes and products. This is because these compounds, depending on the chemical nature, morphology, physical state, size and distribution, can harm both the processes and the mechanical properties of the final product. Thus, the present work aimed to evaluate the modification of alumina inclusions by Ca addition in a medium carbon steel, by modeling the relationship between possible predictor variables in order to establish the fraction of liquid inclusions at the end of the secondary steel refining process. To achieve this goal, it was performed a statistical significance analysis by multiple linear regression. Feature engineering and feature selection methods were used, such as Pearson correlation filtering, “best subsets” method and statistical metrics like p-value, standard error, residuals plot, Durbin–Watson test and adjusted R2. Then, a statistically significant model was finally reached. Through the statistical and metallurgical discussion, the model indicated that the oxidation of steel, the titanium and sulfur content in steel are the main obstacles to the modification of non-metallic inclusions. In addition, computational thermodynamics proved to be an important ally in making decisions about the process. |
| first_indexed | 2024-12-22T01:50:44Z |
| format | Article |
| id | doaj.art-28e290bbae104485b692d736d73fb18a |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-22T01:50:44Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-28e290bbae104485b692d736d73fb18a2022-12-21T18:42:55ZengElsevierJournal of Materials Research and Technology2238-78542021-09-011422572266Analysis of predictors for modification of alumina inclusions in medium carbon steelRaphael Mariano de Souza0Márcia Spelta de Oliveira1José Roberto de Oliveira2Eduardo Junca3Victor Bridi Telles4Felipe Fardin Grillo5PROPEMM, Federal Institute of Espírito Santo (IFES), Vitória, ES, 29040-780, Brazil; Corresponding author.ArcelorMittal Tubarão, Serra, ES, 29160-904, BrazilPROPEMM, Federal Institute of Espírito Santo (IFES), Vitória, ES, 29040-780, BrazilUniversidade do Extremo Sul Catarinense (UNESC), Criciúma, SC, 88806-000, BrazilPROPEMM, Federal Institute of Espírito Santo (IFES), Vitória, ES, 29040-780, BrazilPROPEMM, Federal Institute of Espírito Santo (IFES), Vitória, ES, 29040-780, BrazilCurrently in steelmaking, “inclusions engineering” has occupied a relevant space in terms of improvements in processes and products. This is because these compounds, depending on the chemical nature, morphology, physical state, size and distribution, can harm both the processes and the mechanical properties of the final product. Thus, the present work aimed to evaluate the modification of alumina inclusions by Ca addition in a medium carbon steel, by modeling the relationship between possible predictor variables in order to establish the fraction of liquid inclusions at the end of the secondary steel refining process. To achieve this goal, it was performed a statistical significance analysis by multiple linear regression. Feature engineering and feature selection methods were used, such as Pearson correlation filtering, “best subsets” method and statistical metrics like p-value, standard error, residuals plot, Durbin–Watson test and adjusted R2. Then, a statistically significant model was finally reached. Through the statistical and metallurgical discussion, the model indicated that the oxidation of steel, the titanium and sulfur content in steel are the main obstacles to the modification of non-metallic inclusions. In addition, computational thermodynamics proved to be an important ally in making decisions about the process.http://www.sciencedirect.com/science/article/pii/S2238785421007468Inclusions modificationMultiple linear regressionSecondary refiningStatistical analysis |
| spellingShingle | Raphael Mariano de Souza Márcia Spelta de Oliveira José Roberto de Oliveira Eduardo Junca Victor Bridi Telles Felipe Fardin Grillo Analysis of predictors for modification of alumina inclusions in medium carbon steel Journal of Materials Research and Technology Inclusions modification Multiple linear regression Secondary refining Statistical analysis |
| title | Analysis of predictors for modification of alumina inclusions in medium carbon steel |
| title_full | Analysis of predictors for modification of alumina inclusions in medium carbon steel |
| title_fullStr | Analysis of predictors for modification of alumina inclusions in medium carbon steel |
| title_full_unstemmed | Analysis of predictors for modification of alumina inclusions in medium carbon steel |
| title_short | Analysis of predictors for modification of alumina inclusions in medium carbon steel |
| title_sort | analysis of predictors for modification of alumina inclusions in medium carbon steel |
| topic | Inclusions modification Multiple linear regression Secondary refining Statistical analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2238785421007468 |
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