Prediction of Acicular Ferrite from Flux Ingredients in Submerged Arc Weld Metal of C-Mn Steel

Abstract

The prediction model has been developed for low carbon steel weld metal acicular ferrite microstructure as a function of flux ingredients such as CaO, MgO, CaF 2 and Al 2 O 3 in submerged arc welding carried out at fixed welding parameters. The results of quantitative measurements of acicular ferrite (AF) on eighteen no. of weld metal samples were utilised for developing the prediction model. Among the flux ingredients, CaO appears to be most important as an individual as well as interaction with other ingredients in controlling the amount of acicular ferrite content in the weld metal. Furthermore, formation of acicular ferrite is also related to the weld bead geometry which is influenced by flux ingredients. The prediction equation for acicular ferrite has been checked for adequacy by performing separate experiments on welding using randomly designed flux. The isoresponse curves were developed to show the level of acicular ferrite content at different percentage of flux ingredients.KEY WORDS: submerged arc welding; statiscal experiment for mixture; extreme vertices design; flux ingredients; synergism; antagonism; carbon equivalent; inclusion characteristics; weld bead parameter.plex phenomenon with multiple factors and mechanisms operating simultaneously. The target structure and properties are commonly obtained by adjusting variables such as current, voltage, speed and wire-flux combination by trial and error. Since flux plays an important role in the final weld metal composition and inclusion characteristics in SAW process, understanding the flux composition and microstructure of the weld metal remains a major objective in producing maximum amount of acicular ferrite and thereby optimum mechanical properties.The aim of the present work is to predict acicular ferrite content of the microstructure of weld metal, made with different flux ingredients in CaO-MgO-CaF 2 -Al 2 O 3 flux system using given filler wire and welding parameters in order to provide a rational basis for choosing flux composition. This is important in order to establish flux formulation for optimum mechanical properties. Planning of the ExperimentsThe Idea of this present set of investigations is to take account simultaneous variation of each flux ingredient in a flux mixture in order to find out the effect of each flux ingredient as well as their interaction effects on weld metal microstructural constituent acicular ferrite. This has been made possible by applying "statistical design of experiment" involving mixtures, in particular "extreme vertices design", according to which proportions of the important flux ingredients were varied simultaneously to investigate the simultaneous effect of each flux ingredient and their interactions on weld metal microstructure in CaO-MgOCaF 2 -Al 2 O 3 flux system. · Extreme Vertices Design AlgorithmIn experiments using mixture of q components, the components are expressed as a fraction x of the total mixture and the response is a function of the proportions of the components and not the total amount of mix...

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.…”