Optimization of the Passivation Process for AM 350 and CUSTOM 450 Stainless Steels Using Taguchi Methodology and Gray Relational Analysis

This study presents research on optimizing the parameters of the passivation process for precipitation-hardening stainless steels (PHSS) to improve the corrosion resistance of AM350 and CUSTOM 450 alloys, which are extensively utilized in the aerospace and aviation sectors, since, as this is a complex process, it requires the implementation of a robust methodological approach that allows for multi-response optimization. Experiments were designed using the Taguchi method, which offered a strong framework for examining the impact of material, type of passivation solution, concentration, temperature, and passivation process time on the corrosion resistance of both PHSS alloys. To confirm the ideal PHSS passivation process parameters and measure the significance of each component, gray relational analysis (GRA) and analysis of variance (ANOVA) were also employed. The combined use of the Taguchi/GRA represents a robust and efficient methodological approach to the multi-response optimization of complex processes, overcoming the limitations inherent in the individual application of each technique. It was determined that the optimized parameters were a PHSS AM 350, a solution composed of a combination of citric acid and oxalic acid, acid concentration of 25% v/v, temperature of 50 °C, and time of 120 min. This combination of parameters resulted in significant improvements of up to 55% in corrosion resistance in the H2SO4 and NaCl evaluation solutions, demonstrating the effectiveness of the optimized conditions. This work emphasizes the efficacy of integrating Taguchi, GRA, and ANOVA techniques to significantly reduce the corrosion rate of PHSS undergoing the passivation process using alternatives to nitric acid. The integration of the Taguchi methodology with GRA enables the normalization and combination of responses with different scales and performance criteria into a single gray relational index, facilitating the overall evaluation of the system.