Dr Sushant Rath - Academia.edu (original) (raw)

Papers by Dr Sushant Rath

Research paper thumbnail of Optimization of hot rolling parameters of CRNO steel with the aid of hot compression test and deformation map

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.

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Research paper thumbnail of A Numerical Investigation of Extrusion through Bezier Shaped Curved Die Profile

Key Engineering Materials, Jun 1, 2010

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Research paper thumbnail of Computer Simulation of Hot Rolling of Flat Products

Software engineering, Jan 24, 2017

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Research paper thumbnail of Simulation of plate rolling process using finite element method

Materials Today: Proceedings, 2021

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.

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Research paper thumbnail of Prediction of Impact Energy of Steel Using Artificial Neural Network

Communications in computer and information science, 2022

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Research paper thumbnail of Machine Learning in the Steel Industry

Apple Academic Press eBooks, May 23, 2022

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Research paper thumbnail of A Numerical Analysis of Extrusion of Square Section from Round Billet Through Mathematically Contoured Die with Design of Die Profile

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Research paper thumbnail of Methodology of developing mathematical-ANN hybrid model based mill setup model for hot strip rolling

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.

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Research paper thumbnail of Investigation on the genesis of the shape deformation of plates in New Plate mill, Rourkela Steel Plant

Materials Today: Proceedings, 2020

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Research paper thumbnail of Hybrid Modeling of Flat Rolling Process during Hot Rolling

Materials Science Forum, Jul 1, 2013

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Research paper thumbnail of Application of Artificial Neural Network for Flow Stress Modelling of Steel

American Journal of Neural Networks and Applications, 2017

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Research paper thumbnail of Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force During Hot Rolling of Steel

International Journal of Computational Materials Science and Engineering, Mar 1, 2013

Accurate prediction of roll force during hot strip rolling is essential for model based operation... more Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

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Research paper thumbnail of A Framework for Adaptive Online Thickness Control at Plate Mill of Bhilai Steel Plant

Materials and Manufacturing Processes, Mar 22, 2010

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Research paper thumbnail of Application of machine learning algorithms for prediction of sinter machine productivity

Machine learning with applications, Dec 1, 2021

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Research paper thumbnail of Optimization of hot rolling parameters of CRNO steel with the aid of hot compression test and deformation map

International Journal of Materials Research

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...

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Research paper thumbnail of Application of machine learning (Linear Regression) algorithms for prediction of sinter machine productivity

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.

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Research paper thumbnail of Sinter machine productivity

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.

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Research paper thumbnail of Simulation of plate rolling process using finite element method

Materials Today: Proceedings, 2021

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.

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Research paper thumbnail of Computer Simulation of Hot Rolling of Flat Products

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...

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Research paper thumbnail of Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

Journal of Computer Science Research, 2020

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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Research paper thumbnail of Optimization of hot rolling parameters of CRNO steel with the aid of hot compression test and deformation map

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.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Numerical Investigation of Extrusion through Bezier Shaped Curved Die Profile

Key Engineering Materials, Jun 1, 2010

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Computer Simulation of Hot Rolling of Flat Products

Software engineering, Jan 24, 2017

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Simulation of plate rolling process using finite element method

Materials Today: Proceedings, 2021

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.

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Research paper thumbnail of Prediction of Impact Energy of Steel Using Artificial Neural Network

Communications in computer and information science, 2022

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Research paper thumbnail of Machine Learning in the Steel Industry

Apple Academic Press eBooks, May 23, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Numerical Analysis of Extrusion of Square Section from Round Billet Through Mathematically Contoured Die with Design of Die Profile

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Methodology of developing mathematical-ANN hybrid model based mill setup model for hot strip rolling

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.

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Research paper thumbnail of Investigation on the genesis of the shape deformation of plates in New Plate mill, Rourkela Steel Plant

Materials Today: Proceedings, 2020

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Research paper thumbnail of Hybrid Modeling of Flat Rolling Process during Hot Rolling

Materials Science Forum, Jul 1, 2013

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Research paper thumbnail of Application of Artificial Neural Network for Flow Stress Modelling of Steel

American Journal of Neural Networks and Applications, 2017

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Research paper thumbnail of Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force During Hot Rolling of Steel

International Journal of Computational Materials Science and Engineering, Mar 1, 2013

Accurate prediction of roll force during hot strip rolling is essential for model based operation... more Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

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Research paper thumbnail of A Framework for Adaptive Online Thickness Control at Plate Mill of Bhilai Steel Plant

Materials and Manufacturing Processes, Mar 22, 2010

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Application of machine learning algorithms for prediction of sinter machine productivity

Machine learning with applications, Dec 1, 2021

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Research paper thumbnail of Optimization of hot rolling parameters of CRNO steel with the aid of hot compression test and deformation map

International Journal of Materials Research

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...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Application of machine learning (Linear Regression) algorithms for prediction of sinter machine productivity

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.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Sinter machine productivity

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.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Simulation of plate rolling process using finite element method

Materials Today: Proceedings, 2021

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.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Computer Simulation of Hot Rolling of Flat Products

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...

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Research paper thumbnail of Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

Journal of Computer Science Research, 2020

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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