A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm

Mahfouf, Mahdi; Gaffour, Sidahmed

doi:10.1016/j.engappai.2014.08.014

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…The important prediction results reported here mostly exhibit nonlinear relations . Figure and show combined effect of C, N, Nb, and Ti on the mechanical property of investigated steel.…”

Section: Mechanical Property Prediction Modelingmentioning

confidence: 62%

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Zhou

Cao

et al. 2018

steel research int.

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To solve the contradiction between consumer's personal demand and mass production of steel enterprise, the optimal design for hot strip rolling of steel attracts researcher's attention. The core of the ensemble system is the artificial neural network (ANN) model, which is based on the industrial data. However, industrial data are difficult to be used for modeling because of their high dimension, low quality, and unbalanced. In current work, the method for industrial data processing is proposed for microalloyed steel. Based on the treated data, the mechanical property prediction models are established with the relative error of ±8% for yield strength (YS), ±6% for tensile strength (TS), and the absolute error of ±4% for elongation (EL), respectively. Combined with the multi‐objective optimization algorithm, the ANN models are implemented for designing the hot rolling process for S360 grade steel to control the stability of product quality.

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“…The important prediction results reported here mostly exhibit nonlinear relations . Figure and show combined effect of C, N, Nb, and Ti on the mechanical property of investigated steel.…”

Section: Mechanical Property Prediction Modelingmentioning

confidence: 62%

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Zhou

Cao

et al. 2018

steel research int.

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“…According to the dephosphorization thermodynamics and practical operation in Consteel electric furnace, the end-point phosphorus content is mainly determined by 17 process variables, including the chemical composition of hot metal, hot metal weight, scrap weight, lime weight, dolomite weight, carbon powder weight, smelting cycle, limestone weight, oxygen consumption, natural gas consumption, electricity consumption, end-point C content and end-point temperature. Because the industrial data con-tains numerous noise data which would disturb the model construction and result in incorrect results, 14,15) box-plot method is employed in this paper to filter the exceptional data out, and for the preprocessed data, the main statistics information of the process parameters is shown in Table 1. The symbols from X 1 to X 17 present the input process variables, and Y is the output variable.…”

Section: Collection Of Main Data Parametersmentioning

confidence: 99%

Prediction Model of End-point Phosphorus Content in Consteel Electric Furnace Based on PCA-Extra Tree Model

2021

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According to the actual industrial data from a Consteel electric furnace, a prediction model based on the principal component analysis (PCA) and extremely randomized trees (Extra Tree model) is proposed for end-point phosphorus content. PCA is used to reduce the dimensionality of the input variable affecting the end-point phosphorus content and eliminate the collinearity among the input variables, and then the data transformed by PCA are used as input data for the established Extra tree model. Compared with other feature pre-processing methods, PCA method can greatly improve the regression prediction performance of the Extra Tree model. Finally, the validation by test set indicates that for the PCA-Extra Tree model, the hit rates of end-point phosphorus content are 98%, 96% and 89% with the prediction error range of ± 0.005%, ± 0.004% and ± 0.003%, respectively. The combined PCA-Extra Tree model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for the end-point control and judgment of Consteel electric furnace.

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“…[1][2][3][4][5][6] The literature review reveals that tensile strength and elongation optimization of steel products in general incorporates multi-criteria optimization approaches, 1,2,[7][8][9] based also on artificial intelligence methods. [10][11][12][13][14] This is the case for rated material properties in both quantitative and qualitative terms. 1,10 The required tensile strength and elongation are obtained by changing:…”

Section: Introductionmentioning

confidence: 99%

“…2,7,14 The article presents the practical implementation of tensile strength and elongation optimization for longrolled products made of 16MnCrS5 steel, which is generally used for the fabrication of case-hardened machine parts for several applications (e.g., bars, rods, plates, strips, forgings).…”

Section: Introductionmentioning

confidence: 99%

“…• chemical composition, 2,12,14 • plastic deformation parameters (i.e., influencing microstructure, grain size), 8,[15][16][17] • heat-treatment parameters after plastic deformation. 2,7,14 The article presents the practical implementation of tensile strength and elongation optimization for longrolled products made of 16MnCrS5 steel, which is generally used for the fabrication of case-hardened machine parts for several applications (e.g., bars, rods, plates, strips, forgings).…”

Section: Introductionmentioning

confidence: 99%

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Increasing the tensile strength and elongation of 16MnCrS5 steel using genetic programming

Kovačić¹,

Turnšek²,

Ocvirk³

et al. 2017

Mater. Tehnol.

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[tore Steel Ltd. is one of the largest spring-steel producers in Europe. [tore Steel makes more than 1400 steel grades of different chemical composition. Among them is 16MnCrS5 steel. It is generally used for the fabrication of case-hardened machine parts for various applications (e.g., bars, rods, plates, strips, forgings), where a combination of wear resistance, toughness and dynamic strength is essential. These properties can be easily correlated with tensile strength, which depends on the chemical composition and heat treatment after rolling. In addition, the elongation should be taken into account. In the paper, modeling of tensile strength and elongation with genetic programming is presented and compared with linear regression modeling. The chemical composition data (content of C, Mn, S and Cr) and the heat-treatment regime data (GKZ or BG annealing) were used for modeling. The modeling results show that a higher tensile strength with improved elongation were achieved. Keywords: 16MnCrS5, tensile strength, elongation, modeling, genetic programming, linear regression [tore Steel je najve~ji proizvajalec vzmetnega jekla v Evropi. [tore Steel izdeluje ve~kot 1400 razli~nih kvalitet jekla z razli~nimi kemijskimi sestavami. Med njmi tudi 16MnCrS5, ki spada v skupino jekel za cementacijo, ki so namenjena za strojno obdelavo razli~nih delov (npr. palic, plo{~, trakov, odkovkov), kjer se zahteva kombinacija obrabne odpornosti,`ilavosti ter trajnonihajne trdnosti. Le-te lastnosti lahko povezujemo z natezno trdnostjo, ki je odvisna predvsem od kemi~ne sestave in toplotne obdelave po valjanju. Prav tako je pomemben raztezek. V~lanku je predstavljeno modeliranje natezne trdnosti in raztezka s pomo~jo genetskega modeliranja in linearne regresije. Za modeliranje smo uporabili vsebnosti kemijskih elementov (C, Mn, S in Cr) ter na~in toplotne obdelave (GKZ ali BG). Glede na rezultate smo pove~ali natezno trdnost pri izbolj{anem raztezku.

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A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm

Cited by 9 publications

References 15 publications

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Prediction Model of End-point Phosphorus Content in Consteel Electric Furnace Based on PCA-Extra Tree Model

Increasing the tensile strength and elongation of 16MnCrS5 steel using genetic programming

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