Numerical Simulation and Microstructural Correlation of Duplex Stainless Steel Solidification by Spray-Forming Process

Abstract

Duplex stainless steel are designed to provide better corrosion resistance, particularly chloride stress corrosion and chloride pitting corrosion, and higher strength than standard austenitic stainless steels. In Spray Forming processing, the gas atomizes a liquid metal stream and is generated a number of high velocity gas jets and is broken up into fine drops which solidify in flight and finally is captured the atomized metal spray of alloy onto a moving substrate into near net shape solid. In this research was be presented a thermal model for modeling the heat flow distribution during solidification process and the heat transfer analysis using finite element modeling was carried out, to study of solidification of droplet deposition in spray-forming process and its correlation with microstructure. For this purpose, was assumed a Gaussian distribution of droplet mass flux about the spray cone axis, boundary conditions non-linear of heat transfer coefficient and thermophysical properties dependent with temperature, the spray forming process parameters, such as, fluid pressure, geometrical parameter of the nozzle’s dimensionless characteristic, temperature of the saturation, workpiece height, thickness of the bottom’s sprayer were considerate too. As result were presented a solidification process analysis, study of thermal gradient, cooling curves analysis and correlation between numerical simulation and microstructural result. The microstructure analysis was used to establish the correlation with numerical simulation for has better performance of prevision. Duplex stainless steels are applied in various strategic areas, which are finding greater application in the chemical, oil and gas industries, petrochemical process plants, pollution control equipment, etc. Numerical simulation is a powerful tool to study the spray-forming process, whose parameters that involve this process can save runtime experimental and cost of equipment, besides, predict the result.