Prediction of submerged arc weld‐metal composition from flux ingredients with the help of statistical design of mixture experiment

Abstract

A prediction model has been developed for submerged arc weld-metal chemical composition in terms of flux ingredients with the help of statistical experiments for mixture (extreme vertices design). Bead-on-plate weld deposits as per statistical mixture design experiments were performed at the following welding parameters: current (400 A), voltage (26 V), speed (4.65 mm/s) and electrode extension (30 mm) using CaO-MgO-CaF 2 -Al 2 O 3 flux system. The results show that some of the individual flux ingredients and their binary mixtures have a predominant effect on weld-metal oxygen, manganese, silicon, sulphur, nickel and carbon content. The predicted results show a reasonably good agreement with the experimental results, which were obtained by performing the actual experiments based on a randomly designed flux. Analysis of the experimental data indicate that several mechanisms such as basicity index, oxygen potential, oxide stability, viscosity, electrode chemical reaction, kinetics of slag-metal reaction, etc. are operating simultaneously to yield the final weld-metal composition.

Weld-metal property optimization from flux ingredients through mixture experiments and mathematical programming approach

“…The quality of weld-metal is often evaluated by many characteristics such as chemical composition, mechanical properties, bead profile and microstructure. Studies have shown that these characteristics are influenced by the welding flux formulation; therefore it is important to select the right type of welding flux ingredients and choose the appropriate proportions of the various flux ingredients to attain a good weld-metal quality [2][3][4][5][6][7][8][9][10] . The conventional approach to welding flux development is by experimental optimization.…”

“…The XVERTD method is a proven approach that is sufficient not only to fit the proposed model but also allows a test of model adequacy from a minimal experimental efforts 14 . Kanjilal and his co-investigators [5][6][7][8] used their experimental data to develop prediction models for the measured responses such as weld-metal composition, mechanical properties, microstructure and element transfer characteristics of the flux. However, they did not use the models to perform optimization on the responses.…”

“…Kanjilal et al [5][6][7][8] . In the XVERTD technique, the constraints on the q flux ingredients define the experimental region, which is usually a (q -1) dimensional simplex.…”

“…Because the integration of MP with XVERTD for welding flux formulation is novel, we give a brief systematic procedure for its application. Next, a numerical example is solved using the confirmed models of Kanjilal and his coinvestigators [5][6][7][8] .…”

“…Welding flux design is one of the key areas of arc welding technology that require improvement because the weldmetal quality, productivity of the welding process and economical weld production depend largely on the flux formulation. Operational characteristics such as arc initiation and stability, minimum spatter, positional welding, high deposition rate, penetration and bead morphology are influenced by the welding flux formulation [2][3][4][5][6][7][8] . The quality of weld-metal is often evaluated by many characteristics such as chemical composition, mechanical properties, bead profile and microstructure.…”