Dr Swadesh Kumar Singh
Professor
| Mechanical Engineering
Gokaraju Rangaraju Institute of Engineering & Technology
Telangana
Expertise:
Mechanical Engineering
152
Publications
17
Projects
3106
Scopus Citations
3144
CrossRef
19 Years 8 Months Total Experience
Scopus
3,106
Citations
30
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CrossRef
3,144
Citations
29
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Professional Recognition
Career Award for young teachers
2011
AICTE
Young scientist award
2012
DST
Research Award
2016
UGC
Community & Membership
Board of Studies
memberThe Minerals, Metals & Materials Society
Life membershipSheet metal Forming Research Association
Life membershipPersonal Details
Doctor of Philosophy
2005
N/A
Master of Technology
1998
Indian Institute of Technology, Indore
Professor
Jan 2007 – Present
Gokaraju Rangaraju Institute of Engineering & Technology
| Mechanical Engineering
Engineering and Technology
Scholarly Work
Investigation of Dynamic Deformation behavior of Tungsten heavy alloys (WHAs) for Defence application
CompletedEnhancement of Mechanical properties and forming behavior of AZ31alloy using shear deformation technique for automotive and aerospace application
CompletedTheoretical and simulation based approaches to determine the forming limit diagrams of Ti-6Al-4V at elevated temperatures
CompletedStrengthening Research Facilities in Science Departments of college
CompletedEnhancement of Mechanical Properties of Ti-6Al-4V using Constrained Groove Pressing & Heat Treatment
CompletedForming of Austenitic Stainless Steel (ASS) 304 in Superplastic region
CompletedStudy the Formability of Friction on Formability of EDD steel at elevated temperatures
CompletedComparison of Formability and Texture studies of Zr alloy-4 sheet when produced by rolling and pilgering route
CompletedUnderstanding Forming behavior of Ti-6Al‐4V Material between 400-850C in deep drawing set up
CompletedConstruction of qualitative warm working processing maps for ASS 304 material
CompletedFormability study of AUSTENITIC STAINLESS STEEL (316 GRADE) at higher temperatures and development of redrawing facility in warm conditions of high strength materials
CompletedDevelopment of a new biodegradable friction material (Eco-friendly
CompletedStudy of Finite Element Simulation on the extent of ironing and microstructure of low carbon steels under warm conditions
CompletedCharacterization of Al alloy at elevated temperatures
CompletedFE Simulation of Warm Forming of low C steel material
CompletedDynamic Strain Ageing Behavior In Austenitic Stainless Steel (304 & 316 Grades
CompletedFlow Forming of EDD
CompletedBio-adhesives for the manufacturing of particle board
Experimental and simulation on forming process of high strength sheet metal alloy at various temperatures under warm conditions
Construction of FLD for EDD steel at various temperatures
Formability of Zircalloy
Formability of ASS316L at subzero and superplastic region
Effect of Friction on Formability of ASS304 in superplastic region
Formability of Brass at elevated temperature
Enhancement of Mechanical Properties and forming behavior of AZ31 alloy
HAND HELD SPIROMETER SYSTEM
Swadesh K Singh
-
Patent No. :
345721
Filed :
18-09-2018
Published :
31-08-2020
Granted
Development and Design Considerations in warm forming of extra deep drawing (EDD) Metal Sheet
Swadesh Kumar Singh
-
Patent No. :
3028/CHE/2011
Filed :
02-09-2011
Published :
21-08-2015
Granted
Scholarly Publications
Numerical investigations of constitutive tensile behaviour of materials and wrinkling of cold-rolled aluminium sheet when deep drawn through a Tractrix die
Tooling design and development of set-up for hydro-mechanical deep drawing
Effect of process parameters on product surface finish and thickness variation in hydro-mechanical deep drawing
Optimization of the design parameters of modified die in hydro-mechanical deep drawing using LS-DYNA
Numerical simulation of hydro-mechanical deep drawing - A study on the effect of process parameters on drawability and thickness variation
Application of a neural network to predict thickness strains and finite element simulation of hydro-mechanical deep drawing
Numerical prediction of limiting draw ratio and thickness variation in hydromechanical deep drawing
A comparison of different neural network training algorithms for hydromechanical deep drawing
Showing 1 to 8 of 152 publications