Steel Authority of India

About: Steel Authority of India is a based out in . It is known for research contribution in the topics: Microstructure & Ultimate tensile strength. The organization has 797 authors who have published 661 publications receiving 9958 citations. The organization is also known as: SAIL.

Process for thermorming a three-dimensionally curved panel

Abstract: A device for making panels with three-dimensional curvature from a sheet material, includes a forming impression ( 5 ) defined by a membrane ( 6 ) including an elastically stretchable skin ( 11 ), preferably in combination with a skeleton formed by a grid pattern of flexurally elastic battens ( 10 ) which are mobile in longitudinal translation relative to the skin, and a set of elements for selectively deforming the impression acting on the battens ( 10 ) of the membrane to impart thereto an adaptable three-dimensional curvature. The invention is particularly useful for making sails.

Multi-output mode charger

Abstract: A multi-mode output charger is to support different kinds of batteries for the mobile telecommunication facility It comprises a charger and a charge unit Users can choose different charging voltages and charging currents with a voltage and current regulating switch of the charger Therefore, the charger can support different voltage and current combinations for batteries And the charge unit has several kinds of charge connecting set for different brand batteries Also, the charge unit can extend to engage with different mode charge connecting set for enlarging its supporting range

Development of Fine-Grained, Low-Carbon Bainitic Steels with High Strength and Toughness Produced Through the Conventional Hot-Rolling and Air-Cooling

Abstract: Low-carbon bainitic steels have created enormous interest among scientists across the world in the past few decades because of their high strength, toughness, and weldability replacing the conventional quenched and tempered medium-carbon steels. Three experimental steels with varying alloy additions were made in a 100-kg laboratory induction furnace and cast into 100-mm-diameter cylindrical ingots. These ingots were hot-rolled and air-cooled to 6-mm plates in an experimental rolling mill with selected thermomechanical parameters. Steels processed through this process provided an ultrafine low-carbon bainitic microstructure with maximum yield strength (YS) and ultimate tensile strength (UTS) 575 and 705 MPa, respectively. The Charpy impact toughness of the experimental steels was excellent, and at 253 K (–20 °C), it varied from 114 to 170 Joules. Cu-B-added steel was found to give an optimum combination of strength, YS-575 MPa, and toughness, 114 J at 253 K (–20 °C). Thus, fine-grained, low-carbon bainitic steels could be developed with a proper combination of alloying elements and thermomechanical parameters even by air-cooling.

Effect of temperature and strain distribution on martensitic transformation during uniaxial testing of AISI-304 stainless steel

Abstract: A coupled finite element method has been used to determine the true plastic strain, effective strain, and temperature distribution inside the tensile specimen of AISI-304 austenitic stainless steel during uniaxial testing at low and high strain rates. The volume fraction of martensite has been computed along the gage length by employing Olson-Cohen analysis and using the value of a and β parameters from Heckers curve at the temperatures which were obtained by FEM analysis in different elements of the specimen. The results reveal that due to nonhomogeneous distribution of plastic strain and variation in temperature along the gage length, the volume fraction of martensite would be different near the end of gage length and the center of the specimen.

Mathematical-artificial neural network hybrid model to predict roll force during hot rolling of steel

Abstract: 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.