Recrystallization and Grain Growth of AISI 904L Super-Austenitic Stainless Steel: A Multivariate Regression Approach

Abstract

AISI 904L is a super-austenitic stainless steel that is remarkable for its mechanical properties and high corrosion resistance, which strictly depend on its chemical composition and microstructural features. The recrystallization process and grain growth phenomena play key roles in achieving high levels of material quality, as often requested by customers for specific applications. In this paper, the evolution of the microstructure and hardness values after cold rolling and subsequent annealing is reported, with the aim of optimizing the thermomechanical treatment conditions and improving the efficiency of the production process. The investigation was focused on three different cold reduction ratios (50%, 70% and 80%), while combining different annealing temperatures (950, 1050 and 1150 °C) and soaking times (in the range of 20–180 s. The test results were organized using a data analysis and statistical tool, which was able to show the correlation between the different variables and the impacts of these on recrystallization and grain growth processes. For low treatment temperatures, the tested soaking times led to partial recrystallization, making this condition industrially unattractive. Instead, for the higher temperature, full recrystallization was achieved over a short time (20–40 s), depending on the reduction ratio. Regarding the grain growth behavior, it was found to be independent of the reduction ratio; for each treatment temperature, the grain growth showed a linear trend as a function of the soaking time only. Moreover, the static recrystallization kinetics were analyzed using a statistical analysis software program that was able to provide evidence indicating the most and least influential parameters in the process. In particular, taking into consideration the hardness values as output data, the temperature and soaking time were revealed to have major effects as compared with the reduction ratio, which was excluded from the statistical analysis. The prediction approach allowed us to formulate a regression equation in order to correlate the response and terms. Moreover, a response optimizer was used to predict the best solution to get as close as possible to the hardness target required by the market.