Zexian Deng, Yungui Zhang, Lin Zhang, Junqiang Cong
{"title":"A Transformer and Random Forest Hybrid Model for the Prediction of Non-metallic Inclusions in Continuous Casting Slabs","authors":"Zexian Deng, Yungui Zhang, Lin Zhang, Junqiang Cong","doi":"10.1007/s40192-023-00312-8","DOIUrl":null,"url":null,"abstract":"<p>Non-metallic inclusions (NMIs) in continuous casting slabs will significantly reduce the performance of final steel products and lead to other defects in steel products. The traditional detection methods of NMIs in continuous casting slabs have the problem of low efficiency, and it is complicated to establish a prediction model of NMIs based on physics and chemistry. Therefore, we tried to use the machine learning method by integrating Transformer and Random Forest and established an RF-1DViT model to predict NMIs in continuous casting slabs. To predict the occurrence and the location of NMIs as accurately as possible, the whole process data of steelmaking, refining and continuous casting were used, and the continuous casting slab was processed in slices. The experimental results show that the proposed RF-1DViT model has an F1 score of 0.8991, surpassing Logical Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, AdaBoost, GradientBoost, Multi-Layer Perceptron and 1DViT model, and has good interpretability and strong feature extraction ability. By means of the Random Forest and histogram, the process importance can be analyzed and rules of inclusions generation can be given. The t-SNE manifold learning method can further assist researchers to accurately locate the defect.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"20 5","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrating Materials and Manufacturing Innovation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40192-023-00312-8","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Non-metallic inclusions (NMIs) in continuous casting slabs will significantly reduce the performance of final steel products and lead to other defects in steel products. The traditional detection methods of NMIs in continuous casting slabs have the problem of low efficiency, and it is complicated to establish a prediction model of NMIs based on physics and chemistry. Therefore, we tried to use the machine learning method by integrating Transformer and Random Forest and established an RF-1DViT model to predict NMIs in continuous casting slabs. To predict the occurrence and the location of NMIs as accurately as possible, the whole process data of steelmaking, refining and continuous casting were used, and the continuous casting slab was processed in slices. The experimental results show that the proposed RF-1DViT model has an F1 score of 0.8991, surpassing Logical Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, AdaBoost, GradientBoost, Multi-Layer Perceptron and 1DViT model, and has good interpretability and strong feature extraction ability. By means of the Random Forest and histogram, the process importance can be analyzed and rules of inclusions generation can be given. The t-SNE manifold learning method can further assist researchers to accurately locate the defect.
期刊介绍:
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.