V. Gunaraj

Bio: V. Gunaraj is an academic researcher from Kumaraguru College of Technology. The author has contributed to research in topics: Welding & Submerged arc welding. The author has an hindex of 7, co-authored 8 publications receiving 1054 citations.

Prediction and optimization of weld bead volume for the submerged arc process. Part 2

Abstract: As a part of a study and analysis on the effects of process parameters on weld bead volume in submerged arc welding (SAW) of pipes, mathematical models were developed to relate the process parameters and the weld bead quality parameters. Further, the optimization of weld bead volume was carried out using the optimization module available in the MATLAB version 4.2b software package. The mathematical models thus developed for optimization are also helpful in predicting the weld bead quality parameters and in setting process parameters at optimum values to achieve the desirable weld bead quality at a relatively low cost with a high degree of repeatability and increased production rate. Total volume of the weld bead, an important bead parameter, was optimized (minimized), keeping the dimensions of the other important bead parameters as constraints, to obtain sound and superior quality welded pipes. Sensitivity analysis was also carried out to predict the direct and few interaction effects of important bead parameters on the total volume of the weld bead, and the results are presented in graphical form. The results of the sensitivity analysis are very useful in understanding the interdependence of various weld bead quality parameters in controlling the volume of the weld bead, to improve weld quality, to increase productivity with the available welding facilities and to minimize the total welding cost.

Prediction and optimization of weld bead volume for the submerged arc process - Part 1

Abstract: Because of its high quality and reliability, submerged arc welding (SAW) is one of the chief metal-joining processes employed in industry for the manufacture of steel pipes used for various applications. This paper highlights a study and analysis of various process-control variables and important weld bead quality parameters in SAW of pipes manufactured out of structural steel (IS: 2062). Mathematical models were developed for the submerged arc welding of 6-mm-thick structural steel plates using 3.15-mm-diameter steel electrodes. The models were developed using the five-level factorial technique to relate the important process-control variables -welding voltage, wire feed rate, welding speed and nozzle-to-plate distance - to a few important bead-quality parameters - penetration, reinforcement, bead width, total volume of the weld bead and dilution. The models developed were checked for their adequacy with the F test. Using the models, the main and interaction effects of the process-control variables on important bead geometry parameters were determined quantitatively and presented graphically. The developed models and the graphs showing the direct and interaction effects of process variables on the bead geometry are very useful in selecting the process parameters to achieve the desired weld-bead quality. Also, the precision ofthe results obtained with the mathematical models were tested by using conformity test runs. The test runs were conducted nearly two years after the development of mathematical models with the same experimental setup, and it was found the accuracy of the predicted results is about 98%. Further, these mathematical models help to optimize SAW to make it a more cost-effective process.

Removal of basic dye Auramine-O by ZnS:Cu nanoparticles loaded on activated carbon: optimization of parameters using response surface methodology with central composite design

Abstract: This research is focused on the ultrasound-assisted removal of Auramine-O (AO) dye from aqueous solutions using ZnS:Cu nanoparticles loaded on activated carbon (ZnS:Cu-NP-AC) as an adsorbent. ZnS:Cu nanoparticles were synthesized and characterized using FESEM (Field-Emission Scanning Electron Microscopy) and XRD (X-Ray Diffraction) analysis. The experiments were designed by response surface methodology. A quadratic model was used to predict the variables. Analysis of variance was used for investigation of variables and interaction between them. High F-value (48.91), very low P-value (<0.00001), non-significant lack of fit, and the determination coefficient (R2 = 0.977) demonstrate good correlation between experimental and predicted values of the response. The highest removal percent attained was 99.76%, and the optimum parameters achieved are: adsorbent amount (0.02 g), initial dye concentration (20 mg L−1), sonication time (3 min) and pH = 7. Adsorption processes of AO by ZnS:Cu-NP-AC could be well described by a Langmuir isotherm and a pseudo-second-order kinetic model. The maximum adsorption capacity of AO by ZnS:Cu-NP-AC was determined as 183.15 mg g−1, suggesting a highly promising potential for ZnS:Cu-NP-AC to be used as a new adsorbent.

Application of Taguchi and response surface methodologies for geometric error in surface grinding process

Abstract: The geometric error in the surface grinding process is mainly affected by the thermal effect and the stiffness of the grinding system. For minimizing the geometric error, the selection of grinding parameters is very important. This paper presented an application of Taguchi and response surface methodologies for the geometric error. The effect of grinding parameters on the geometric error was evaluated and optimum grinding conditions for minimizing the geometric error were determined. A second-order response model for the geometric error was developed and the utilization of the response surface model was evaluated with constraints of the surface roughness and the material removal rate. Confirmation experiments were conducted at an optimal condition and selected two conditions for observing accuracy of the developed response surface model.