Influence of high deformation on the microstructure of low-carbon steel

Popa, Florin; Chicinaş, Ionel; Frunză, Dan; Nicodim, Ioan; Banabic, Dorel

doi:10.1007/s12613-014-0905-x

Cited by 9 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The re-apparition of the rounded voids could be connected with the activation of a slip system due to the exceeding of the maximum shear stress. For high cold work a similar behaviour has been evidenced for DC04 steel sheets [14]. In aluminium the precipitates (usually known to exist for this alloy) retard the tendency to form dense dislocation walls [16], behaviour that can also lead to void reduction as the cold work value increases.…”

Section: Resultssupporting

confidence: 65%

“…In automotive industry another largely used material is low carbon steel. Our previously work was focused on the voids and microstructure evolution in the high deformed DC04 steel [14]. For the DC04, changes in ferrite microstructure from equiaxial grain to fibrous structure at 89 % cold work was found and, as well was found that the voids are elongated in the rolling direction.…”

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Voids and Microstructure Evolution in Aluminium Sheets during High Deformation

Popa

Chicinaş

Banabic

2015

AEF

Self Cite

View full text Add to dashboard Cite

Abstract. The aim of the study is to identify the microstructure and voids evolution during the aluminium AA6016-T4 sheet deformation up to 93 % in order to perform deformation simulation for future applications. The deformed samples were cut in the rolling direction and to an angle of 45 and respectively 90 degrees to the rolling direction. The low deformed sample exhibit elliptic voids that are flattening out and for high deformed samples a new generation of rounded voids are generated. The microstructure evolution during cold work was studied by scanning electron microscopy and the distortion of the grain is discussed. The grain size evolution and the shape of grains related to the forming degree is analyzed.

show abstract

Section: Resultssupporting

confidence: 65%

Section: Introductionmentioning

confidence: 95%

Voids and Microstructure Evolution in Aluminium Sheets during High Deformation

Popa

Chicinaş

Banabic

2015

AEF

Self Cite

View full text Add to dashboard Cite

show abstract

“…The slopes of the final yield strength vs reduction rate graphs for different initial yield strengths are exactly the same. Probably, this is because of the effect of grain refinement, which is increasing with reduction rate [3]. Figure 3a shows the effect of reduction rate on final tensile strength with respect to initial sheet thickness.…”

Section: The Effects Of Independent Variables On Final Mechanical Promentioning

confidence: 99%

“…It is well known that new microstructures might be created and new properties might be developed by heat treatment and processing of low carbon steel. Cold rolling reduces the grain size and increases the hardness of low carbon steel and annealing increases the toughness [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Cold Rolled Steel Using Genetic Expression Programming

Kanca

Çavdar

Erşen³

2016

Acta Phys. Pol. A

View full text Add to dashboard Cite

A new model was developed to predict the mechanical properties of St22 grade cold rolled deep drawing steel by gene expression programming. To obtain a dataset to find out the effect of reduction rate on the mechanical properties of cold rolled and galvanized steel sheet, an experimental program was constructed in the real production plant by keeping all other process parameters constant. The training and testing data sets of gene expression programming model were obtained from the test results. For gene expression programming model, mechanical properties (yield strength, ultimate tensile strength and elongation) before cold rolling, chemical composition, initial sheet thickness and reduction rate were used as independent input variables, while mechanical properties after cold rolling (yield strength, ultimate tensile strength and elongation) were used as dependent output variables. Before constructing the gene expression programming models for dependent variables, dataset was analyzed using the analysis of variance and statistically significant (P ≤ 0.1) independent parameters, i.e. initial sheet thickness, reduction rate, initial yield strength, initial tensile strength, elongation and Mn content were used in gene expression programming model. Different models were obtained for each dependent variable depending on the significant independent variables using the training dataset and accuracy of the best models was verified with testing data set.The predicted values were compared with experimental results and it was found that models are in good agreement with the experimentally obtained results.

show abstract

“…To reduce the production cost and the human intervention, the introduction of automation in the steel industry in a more enhanced form than the present one, is necessary. In this perspective, prediction algorithms are used to speed up the production process, and such efforts are being taken up by the materials science and technology community for attaining certain solutions after compilation of extensive datasets of material composition, process methods, and related properties [14][15][16]. In 2009, Brahme et al have designed an artificial neural-network-based prediction model of cold rolling textures from steel, which is used to predict fiber texture using texture intensities, carbon content, carbide content, and the amount of rolling reduction [17].…”

Section: Introductionmentioning

confidence: 99%

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Choudhury

2021

Preprint

View full text Add to dashboard Cite

Prediction of process route from materials with a desired set of property is one of the fundamental issues in the perspective of the materials design aspect. The parameter space is often too large to be bound since there are too many possibilities. However, in the areas with limited theoretical access, artificial learning techniques can be attempted by using the available data of the candidate materials. In this study, a computational method has been proposed to predict the different process routes of steel constituted taking composition and desired mechanical properties as inputs. First of all, historical data of the actual rolling process was collected, cleaned, and integrated. Further, the dataset is divided into four different classes based on the rolling process data. Then, to find out the essential characteristics of variables, feature correlation among various features has been calculated. A state-of-art machine learning prediction methods such as logistic regression, K- nearest neighbor, Support vector machine, and the random forest are studied to implement the prediction model. In order to avoid the overfitting of the model, k fold cross-validation is applied to the model, and achieve a realistic prediction result with an accuracy of 97%. The F1-score of the classification model is 0.86, and the kappa score is 0.95, which comply that the model has excellent learning and speculation ability and the precise forecast of steel process routes based on the given input parameters.

show abstract

Influence of high deformation on the microstructure of low-carbon steel

Cited by 9 publications

References 16 publications

Voids and Microstructure Evolution in Aluminium Sheets during High Deformation

Voids and Microstructure Evolution in Aluminium Sheets during High Deformation

Prediction of Mechanical Properties of Cold Rolled Steel Using Genetic Expression Programming

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Contact Info

Product

Resources

About