Gunaraj-10/00 Zaida

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Specifications	Gunaraj-10/00 Zaida
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286-s| OCTOBER 2000 RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT ABSTRACT. Because of

Specifications	Gunaraj-10/00 Zaida
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Content	286-s\| OCTOBER 2000 RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT/RESEARCH/DEVELOPMENT 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 vari- ous 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 devel- oped 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 in- teraction effects of the process-control variables on important bead geometry parameters were determined quantita- tively and presented graphically. The developed models and the graphs showing the direct and interaction effects of process variables on the bead geome- try are very useful in selecting the process parameters to achieve the desired weld- bead quality. Also, the precision of the re- sults 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 ex- perimental setup, and it was found the accuracy of the predicted results is about 98%. Further, these mathematical mod- els help to optimize SAW to make it a more cost-effective process. Introduction Submerged arc welding is one of the major fabrication processes in industry because of its inherent advantages, in- cluding deep penetration and a smooth bead (Refs.1, 2). In the SAW of pipes, en- gineers often face the problem of select- ing appropriate and optimum combina- tions of input process-control variables for achieving the required weld bead quality or predicting the weld bead qual- ity for the proposed process-control-vari- able values (Ref. 3). For automatic SAW, the control parameters must be fed to the system according to some mathematical formula to achieve the desired results (Ref. 4). These important problems can be solved with the development of math- ematical models through effective and strategic planning, design and execution of experiments. To achieve this, statistically designed experiments based on the factorial tech- nique were used to reduce the cost and time, as well as to obtain the required in- formation about the main and the inter- action effects on the response parameters (Refs. 5, 6). A cross section of a weld bead showing the important weld bead quality parameters is given in Fig. 1. The mathematical models developed are useful for selecting correct process parameters to achieve the desired weld bead quality and to predict weld bead quality for the given process parameters (Ref. 7). These models facilitate opti- mization of the process and sensitivity analysis. They also help to improve the understanding of the effect of process pa- rameters on bead quality, to evaluate the interaction effects of bead parameters and to optimize the bead quality to ob- tain a high-quality welded joint at a rela- tively low cost with high productivity. Prediction and Optimization of Weld Bead Volume for the Submerged Arc Process — Part 1 BY V. GUNARAJ ANDN. MURUGAN The main and interaction effects of the process-control variables on important bead geometry parameters were determined quantitatively and are presented graphically KEY WORDS Dilution SAW Optimization Bead Geometry Weld Bead Penetration Weld Bead Reinforcement Weld Bead Width Design Matrix V. GUNARAJ is Assistant Professor of Me- chanical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India. N. MURUGAN is Assistant Professor of Me- chanical Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India.
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Following Datasheets	10-2000-MATSUSHITA-s (9 pages) 10-2000-MOON-s (8 pages) 10-2000-NELSON-s (11 pages) 10-2002-HUANG-s (12 pages) 10-2002-VIANCO-s (10 pages) 10-2002-XIE-s (8 pages) 10-2003-COLLINS-s (8 pages) 10-2003-GOULD-s (5 pages) 10-2003-MENDEZ-s (11 pages) 10-2003-SOLOMON-s-1 (10 pages)
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