Sudhakaran R
Iam presently working as associate professor in the department of mechanical engineering at SNS College of Engineering, Coimbatore. Iam currently pursuing my Ph.D in the area of welding. I have so far published 7 papers in International Journals and over ten papers in International Conferences.
Phone: 91-9894030121
Address: HOD, Department of Mechanical Engineering, SNS College of Engineering, Coimbatore
Phone: 91-9894030121
Address: HOD, Department of Mechanical Engineering, SNS College of Engineering, Coimbatore
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Papers by Sudhakaran R
the regression model was used to establish a relationship between welding input parameters and depth of penetration for gas tungsten arc welding of 202 grade stainless steel plates. A five level four factor central composite rotatable design (CCRD) with 31 experimental runs was used to conduct the experiments. The process control parameters chosen for the study are welding current (I), welding speed (V), welding gun angle () and shielding gas flow rate (Q). A mathematical model was developed to correlate the
process parameters to depth of penetration. The developed model was then compared with the experimental results; it was found that the deviation falls within the limit of a 95% confidence level. Additionally, the results obtained from the mathematical model were more accurate in predicting depth
of penetration. The direct and interactive effects of the process parameters are also discussed.
Keywords: Depth of penetration, Central composite rotatable design, Analysis of variance, Stainless steel
and mathematical models were developed correlating the important controllable GTAW process parameters like welding current, welding speed, shielding gas flow rate and welding gun angle with weld bead parameters like depth of penetration, bead width and depth to width ratio. Using these models the direct and interaction effects of the process parameters on weld bead geometry were studied. Optimization of process parameters was done using GA. A source code was developed using Turbo C to do the optimization for
depth to width ratio with penetration and bead width as constraints. The optimal process parameters gave a value of 0.9 for depth to width ratio which demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results help in selecting quickly the process parameters to achieve the desired quality.
Keywords: Gas tungsten arc welding, stainless steel, genetic algorithm, weld bead geometry, central composite rotatable design
strength, toughness, corrosion resistance, and phase stability.This paper presents a study on the effect of process parameters on ferrite number in 202 grade stainless steelgas tungsten arc welded plates (GTAW). Experiments were conducted based on response surface methodology. The
ferrite number was determined by using a ferrite scope and by using DeLong diagram. A mathematical model wasdeveloped correlating the important controllable GTAW process parameters like welding gun angle, welding speed,plate length, welding current, and shielding gas flow ratewith ferrite number. The adequacy of the model waschecked using analysis of variance technique. The developed model is very useful to quantitatively determine the
ferrite number. The main and interaction effects of theprocess parameters are presented in graphical form thathelps in selecting quickly the process parameters to achieve the desired results.
Keywords Ferrite number . GTAW . Response surface
methodology . Analysis of variance
composite rotatable design with full replication technique. A mathematical model was developed correlating the process parameters with angular distortion. A source code was developed in MATLAB 7.6 to do the optimization. The optimal process parameters gave a value of 0.0305° for angular distortion which demonstrates the accuracy of the model developed. The results indicate that the optimized values for the process parameters are capable of producing weld with minimum distortion.
Keywords: Particle swarm optimization, Gas tungsten arc welding, Central
composite rotatable design, Angular distortion, Design of experiments
development of neural network model for predicting depth of penetration and optimizing the process parameters for maximizing depth of penetration using simulated annealing algorithm. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding
gun angle. The chosen output parameter was depth of penetration.The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data, feed-forward backpropagation neural network model was developed and trained using Levenberg– Marquardt algorithm. It was found thatANNmodel based on
network 4-15-1 predicted depth of penetration more accurately. A mathematical model was also developed correlating the process parameters with depth of penetration for doing optimization. A source code was developed in MATLAB to do the optimization. The optimized process parameters gave a value of 3.778 mm for depth of penetration.
Keywords Artificial neural networks Fractional factorial Simulated annealing Depth of penetration Gas tungsten arc welding