International Journal of Materials Research, Mar 29, 2023
Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core... more Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core of electrical machines due to low core loss and high magnetic permeability. Stringent market conditions not only require CRNO steel with superior magnetic properties but also demand excellent surface conditions. CRNO steel is cold rolled to 0.5 mm in reversing mill. High hot rolled input thickness (>2.6 mm) increases the number of passes during cold rolling and adversely affects the mill productivity. It also results in surface defects such as buckling and coil break. The flow stress of this steel varies differently compared to conventional rolled steel. Thus, it becomes difficult to optimize the reduction schedule and hence safe hot rolling practice is adopted to restrict roll force within permissible limit resulting in higher thickness. A hot compression test was carried out in a Gleeble–3500 to evaluate the flow stress behaviour of this steel and a deformation map was developed to optimize the hot rolling window. The input from the hot compression test and deformation map was used to develop a mill setup model to accurately predict the roll force and optimize the reduction schedule of CRNO steel in the finishing stands of HSM. The final thickness of hot-rolled coils during industrial trials with an optimized reduction schedule was found to be in the range of 2.4–2.6 mm compared to 2.7–3.0 mm during conventional rolling. These coils were further cold rolled and finished in 4–5 passes compared to 6–7 passes with conventional rolling. Reduction in the number of passes has resulted in increased productivity during cold rolling as well as improved surface finish.
Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic... more Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic deformation of material at roll bite in a hot rolling process. The roll bite stress field significantly changes with change in the composition of workpiece material. In this paper, a study of roll bite deformation during a plate rolling process is carried out for microalloyed grade of steel using DEFORM-3D software. Norton-Hoff flow stress constitutive equation, one of the material characteristics equations inbuilt of the software, was used for the simulations. Coefficients and exponents of the constitutive equation were evaluated using multivariable optimization technique from experimental data generated in Gleeble-3500, a dynamic thermo-mechanical simulator. Input parameters like dimensions of roll, slab and roller tables of an industrial plate mill were incorporated in the preprocessor module of DEFORM-3D software. The FEM software calculates stress, strain, roll force and temperature. The stress distribution at roll bite calculated by DEFORM-3D software for microalloyed grade of steel is compared with that of plain carbon grade of steel. Effect of temperature and coefficient of friction on roll bite stress distribution for microalloyed grade of steel is discussed in the paper. Roll force predicted by the FEM software was validated with measured roll force recorded from load cells of the industrial plate mill. The predicted roll force agrees well with the measured values of roll force.
International Journal for Computational Methods in Engineering Science and Mechanics, Feb 17, 2021
Abstract Hot strip mill, one of the most important units of an integrated steel plant, is operate... more Abstract Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of the material being rolled using mathematical models derived from fundamental principles of heat transfer and plastic deformation. However, such mill setup models often compute inaccurate schedules leading to quality issues and operational problems. This paper describes a novel technique of developing a hybrid model by integrating mathematical models with artificial neural network (ANN) model. The trained hybrid models use a multivariable optimization algorithm to calculate the thermal, reduction and speed schedules during hot strip rolling. More than six hundred coils were successfully rolled in an industrial hot strip mill using the mill setup model developed under the present work. It is found that the mill setup model developed using the hybrid models is more accurate and faster than the mill setup models that use conventional mathematical models.
Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core... more Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core of electrical machines due to low core loss and high magnetic permeability. Stringent market conditions not only require CRNO steel with superior magnetic properties but also demand excellent surface conditions. CRNO steel is cold rolled to 0.5 mm in reversing mill. High hot rolled input thickness (>2.6 mm) increases the number of passes during cold rolling and adversely affects the mill productivity. It also results in surface defects such as buckling and coil break. The flow stress of this steel varies differently compared to conventional rolled steel. Thus, it becomes difficult to optimize the reduction schedule and hence safe hot rolling practice is adopted to restrict roll force within permissible limit resulting in higher thickness. A hot compression test was carried out in a Gleeble–3500 to evaluate the flow stress behaviour of this steel and a deformation map was developed to...
This is a Linear Regression Program in Python to Predict Sinter Plant Productivity of an inegrate... more This is a Linear Regression Program in Python to Predict Sinter Plant Productivity of an inegrated steel plant.
This datasets contains Sinter Machine Productivity as out put and 16 input parameters: (1) I/O Fi... more This datasets contains Sinter Machine Productivity as out put and 16 input parameters: (1) I/O Fines Total, Fe % (2) I/O Fines SiO2, % (3)I/O Fines Al2O3, % (4)I/O Fines CaO, % (5)Flux CaO, % (6)Flux MgO, % (7)Flux Crushing Index, % (8)Coke Crushing Index, % (9)Sinter Total Fe, % (10)Sinter FeO, % (11)Sinter SiO2, % (12)Sinter Al2O3, % (13) Sinter CaO, % (14)Sinter MgO, % (15)Sinter +40mm Size, % (16)Drum Tumbling Index (DTI), % The objective is to correlate the input parameters with sinter plant productivity and suggest prescriptive analytics.
Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic... more Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic deformation of material at roll bite in a hot rolling process. The roll bite stress field significantly changes with change in the composition of workpiece material. In this paper, a study of roll bite deformation during a plate rolling process is carried out for microalloyed grade of steel using DEFORM-3D software. Norton-Hoff flow stress constitutive equation, one of the material characteristics equations inbuilt of the software, was used for the simulations. Coefficients and exponents of the constitutive equation were evaluated using multivariable optimization technique from experimental data generated in Gleeble-3500, a dynamic thermo-mechanical simulator. Input parameters like dimensions of roll, slab and roller tables of an industrial plate mill were incorporated in the preprocessor module of DEFORM-3D software. The FEM software calculates stress, strain, roll force and temperature. The stress distribution at roll bite calculated by DEFORM-3D software for microalloyed grade of steel is compared with that of plain carbon grade of steel. Effect of temperature and coefficient of friction on roll bite stress distribution for microalloyed grade of steel is discussed in the paper. Roll force predicted by the FEM software was validated with measured roll force recorded from load cells of the industrial plate mill. The predicted roll force agrees well with the measured values of roll force.
Hot rolling process of flat products is a complex process involving plastic deformation of steel,... more Hot rolling process of flat products is a complex process involving plastic deformation of steel, multi-mode heat transfer, microstructure evolution and elastic deformation of rolls and strips. Computer simulation of this process is essential for design modifications of mill hardware and optimization of process parameters to achieve desired product quality with minimum processing cost and minimum energy consumption. This paper describes combined use of two commercially available softwares for computers simulation of hot rolling process after necessary customization. DEFORM, a general purpose Finite Element Method (FEM)software, has been customized for simulation of roll-bite deformation; HSMM, a general purpose software for simulation of overall hot rolling process, has been customized for simulation of entire rolling process of a hot strip mill. The roll force predicted by DEFORM software has been validated with experimental rolling mill data before making simulations. Computer sim...
After production of a steel product in a steel plant, a sample of the product is tested in a labo... more After production of a steel product in a steel plant, a sample of the product is tested in a laboratory for its mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and percentage elongation. This paper describes a mathematical model based method which can predict the mechanical properties without testing. A neural network based adaptation algorithm was developed to reduce the prediction error. The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model. Based on the algorithm, an ASP.Net based intranet website has also been developed for calculation of the mechanical properties. In the starting Furnace Module webpage, austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace. In the Mill Module webpage, different conditions of static, dynamic and metadynamic recrystallization are calculated....
International Journal of Materials Research, Mar 29, 2023
Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core... more Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core of electrical machines due to low core loss and high magnetic permeability. Stringent market conditions not only require CRNO steel with superior magnetic properties but also demand excellent surface conditions. CRNO steel is cold rolled to 0.5 mm in reversing mill. High hot rolled input thickness (>2.6 mm) increases the number of passes during cold rolling and adversely affects the mill productivity. It also results in surface defects such as buckling and coil break. The flow stress of this steel varies differently compared to conventional rolled steel. Thus, it becomes difficult to optimize the reduction schedule and hence safe hot rolling practice is adopted to restrict roll force within permissible limit resulting in higher thickness. A hot compression test was carried out in a Gleeble–3500 to evaluate the flow stress behaviour of this steel and a deformation map was developed to optimize the hot rolling window. The input from the hot compression test and deformation map was used to develop a mill setup model to accurately predict the roll force and optimize the reduction schedule of CRNO steel in the finishing stands of HSM. The final thickness of hot-rolled coils during industrial trials with an optimized reduction schedule was found to be in the range of 2.4–2.6 mm compared to 2.7–3.0 mm during conventional rolling. These coils were further cold rolled and finished in 4–5 passes compared to 6–7 passes with conventional rolling. Reduction in the number of passes has resulted in increased productivity during cold rolling as well as improved surface finish.
Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic... more Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic deformation of material at roll bite in a hot rolling process. The roll bite stress field significantly changes with change in the composition of workpiece material. In this paper, a study of roll bite deformation during a plate rolling process is carried out for microalloyed grade of steel using DEFORM-3D software. Norton-Hoff flow stress constitutive equation, one of the material characteristics equations inbuilt of the software, was used for the simulations. Coefficients and exponents of the constitutive equation were evaluated using multivariable optimization technique from experimental data generated in Gleeble-3500, a dynamic thermo-mechanical simulator. Input parameters like dimensions of roll, slab and roller tables of an industrial plate mill were incorporated in the preprocessor module of DEFORM-3D software. The FEM software calculates stress, strain, roll force and temperature. The stress distribution at roll bite calculated by DEFORM-3D software for microalloyed grade of steel is compared with that of plain carbon grade of steel. Effect of temperature and coefficient of friction on roll bite stress distribution for microalloyed grade of steel is discussed in the paper. Roll force predicted by the FEM software was validated with measured roll force recorded from load cells of the industrial plate mill. The predicted roll force agrees well with the measured values of roll force.
International Journal for Computational Methods in Engineering Science and Mechanics, Feb 17, 2021
Abstract Hot strip mill, one of the most important units of an integrated steel plant, is operate... more Abstract Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of the material being rolled using mathematical models derived from fundamental principles of heat transfer and plastic deformation. However, such mill setup models often compute inaccurate schedules leading to quality issues and operational problems. This paper describes a novel technique of developing a hybrid model by integrating mathematical models with artificial neural network (ANN) model. The trained hybrid models use a multivariable optimization algorithm to calculate the thermal, reduction and speed schedules during hot strip rolling. More than six hundred coils were successfully rolled in an industrial hot strip mill using the mill setup model developed under the present work. It is found that the mill setup model developed using the hybrid models is more accurate and faster than the mill setup models that use conventional mathematical models.
Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core... more Cold-rolled non-oriented (CRNO) electrical steels find a wide variety of applications in the core of electrical machines due to low core loss and high magnetic permeability. Stringent market conditions not only require CRNO steel with superior magnetic properties but also demand excellent surface conditions. CRNO steel is cold rolled to 0.5 mm in reversing mill. High hot rolled input thickness (>2.6 mm) increases the number of passes during cold rolling and adversely affects the mill productivity. It also results in surface defects such as buckling and coil break. The flow stress of this steel varies differently compared to conventional rolled steel. Thus, it becomes difficult to optimize the reduction schedule and hence safe hot rolling practice is adopted to restrict roll force within permissible limit resulting in higher thickness. A hot compression test was carried out in a Gleeble–3500 to evaluate the flow stress behaviour of this steel and a deformation map was developed to...
This is a Linear Regression Program in Python to Predict Sinter Plant Productivity of an inegrate... more This is a Linear Regression Program in Python to Predict Sinter Plant Productivity of an inegrated steel plant.
This datasets contains Sinter Machine Productivity as out put and 16 input parameters: (1) I/O Fi... more This datasets contains Sinter Machine Productivity as out put and 16 input parameters: (1) I/O Fines Total, Fe % (2) I/O Fines SiO2, % (3)I/O Fines Al2O3, % (4)I/O Fines CaO, % (5)Flux CaO, % (6)Flux MgO, % (7)Flux Crushing Index, % (8)Coke Crushing Index, % (9)Sinter Total Fe, % (10)Sinter FeO, % (11)Sinter SiO2, % (12)Sinter Al2O3, % (13) Sinter CaO, % (14)Sinter MgO, % (15)Sinter +40mm Size, % (16)Drum Tumbling Index (DTI), % The objective is to correlate the input parameters with sinter plant productivity and suggest prescriptive analytics.
Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic... more Abstract Finite Element Method (FEM) is an efficient tool to study high-temperature elastoplastic deformation of material at roll bite in a hot rolling process. The roll bite stress field significantly changes with change in the composition of workpiece material. In this paper, a study of roll bite deformation during a plate rolling process is carried out for microalloyed grade of steel using DEFORM-3D software. Norton-Hoff flow stress constitutive equation, one of the material characteristics equations inbuilt of the software, was used for the simulations. Coefficients and exponents of the constitutive equation were evaluated using multivariable optimization technique from experimental data generated in Gleeble-3500, a dynamic thermo-mechanical simulator. Input parameters like dimensions of roll, slab and roller tables of an industrial plate mill were incorporated in the preprocessor module of DEFORM-3D software. The FEM software calculates stress, strain, roll force and temperature. The stress distribution at roll bite calculated by DEFORM-3D software for microalloyed grade of steel is compared with that of plain carbon grade of steel. Effect of temperature and coefficient of friction on roll bite stress distribution for microalloyed grade of steel is discussed in the paper. Roll force predicted by the FEM software was validated with measured roll force recorded from load cells of the industrial plate mill. The predicted roll force agrees well with the measured values of roll force.
Hot rolling process of flat products is a complex process involving plastic deformation of steel,... more Hot rolling process of flat products is a complex process involving plastic deformation of steel, multi-mode heat transfer, microstructure evolution and elastic deformation of rolls and strips. Computer simulation of this process is essential for design modifications of mill hardware and optimization of process parameters to achieve desired product quality with minimum processing cost and minimum energy consumption. This paper describes combined use of two commercially available softwares for computers simulation of hot rolling process after necessary customization. DEFORM, a general purpose Finite Element Method (FEM)software, has been customized for simulation of roll-bite deformation; HSMM, a general purpose software for simulation of overall hot rolling process, has been customized for simulation of entire rolling process of a hot strip mill. The roll force predicted by DEFORM software has been validated with experimental rolling mill data before making simulations. Computer sim...
After production of a steel product in a steel plant, a sample of the product is tested in a labo... more After production of a steel product in a steel plant, a sample of the product is tested in a laboratory for its mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and percentage elongation. This paper describes a mathematical model based method which can predict the mechanical properties without testing. A neural network based adaptation algorithm was developed to reduce the prediction error. The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model. Based on the algorithm, an ASP.Net based intranet website has also been developed for calculation of the mechanical properties. In the starting Furnace Module webpage, austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace. In the Mill Module webpage, different conditions of static, dynamic and metadynamic recrystallization are calculated....
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