Abstract
Submerged arc welding (SAW) is one of the chief metal fabrication processes in industry. It works with high current density and can affect high metal deposition rate. The present work emphasizes the influence of process parameters on quality and performance of submerged arc weldment by incorporating one of the traditional methods of statistical data analysis i.e. ANOVA. This approach aims to reveal the main and interactive effects of process parameters on different response variables associated with the weldment. Based on factorial design without replication, experiments were conducted with three different levels of process parameters like voltage, welding current and electrode stick out to obtain butt joints from mild steel plates. Experimental results were examined by exploring Analysis of Variance Method, using statistical software package MINITAB. liased on ANOVA, several graphical plots are shown-histogram of residuals, normal plot of residuals, residual verses order and residual verses specified variables etc. ANOVA delivers feasible data to justify the significance of process parameters on different response variables of submerged arc weldment in terms of their main effects and interactive effects. The effects due to variation of process parameters on (i) bead geometry in terms of bead width, depth of penetration, reinforcement, and (ii) bead quality as well as performance of the welded joint in terms of hardness, impact value and tensile strength are represented graphically. Graphical representations of the experimental data are supposed to contribute 149 Vol. 17, No. 3, 2006 Effect of Process Parameters on Submerged Arc Weldment valuable information for quality control of submerged arc welding process, which would help to obtain superior quality weld and also to achieve high productivity.
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CITATION STYLE
Datta, S., Sundar, M., Bandyopadhyay, A., Nandi, G., Pal, P. K., & Roy, S. C. (2006). Effect of Process Parameters on Bead Geometry, Quality and Performance of Submerged Arc Weldment - Experimental and Statistical Analyses. Journal of the Mechanical Behavior of Materials, 17(3), 149–192. https://doi.org/10.1515/jmbm.2006.17.3.149
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