Uniform corrosion and general dissolution of aluminum alloys was not as well-studied in the past, although it was known for causing significant amount of weight loss. This work comprises four chapters to understand uniform corrosion of aluminum alloys 2024-T3, 6061-T6, and 7075-T6. A preliminary weight loss experiment was performed for distinguishing corrosion induced weight loss attributed to uniform corrosion and pitting corrosion. The result suggested that uniform corrosion generated a greater mass loss than pitting corrosion.
First, to understand uniform corrosion mechanism and kinetics in different environments, a series of static immersion tests in NaCl solutions were performed to provide quantitative measurement of uniform corrosion. Thereafter, uniform corrosion development as a function of temperature, pH, Cl-, and time was investigated to understand the influence of environmental factors. Faster uniform corrosion rate has been found at lower temperature (20 and 40°C) than at higher temperature (60 and 80°C) due to accelerated corrosion product formation at high temperatures inhibiting corrosion reactions. Electrochemical tests including along with scanning electron microscopy (SEM) were utilized to study the temperature effect.
Second, in order to further understand the uniform corrosion influence on pit growth kinetics, a long term exposures for 180 days in both immersion and ASTM-B117 test were performed. Uniform corrosion induced surface recession was found to have limited impact on pit geometry regardless of exposure methods. It was also found that the competition for limited cathodic current from uniform corrosion the primary rate limiting factor for pit growth. Very large pits were found after uniform corrosion growth reached a plateau due to corrosion product coverage. Also, optical microscopy and focused ion beam (FIB) imaging has provided more insights of distinctive pitting geometry and subsurface damages found from immersion samples and B117 samples.
Although uniform corrosion was studied in various electrolytes, the pH impact was still difficult to discern due to ongoing cathodic reactions that changed electrolyte pH with time. Therefore, buffered pH electrolytes with pH values of 3, 5, 8, and 10 were prepared static immersion tests. Electrochemical experiments were performed in each buffered pH conditions for understanding corrosion mechanisms. Uniform corrosion was found exhibiting higher corrosion rate in buffered acidic and alkaline electrolytes due to pH- and temperature-dependent corrosion product precipitation. Observations were supported by electrochemical, SEM, and EDS observations.
Due to the complexity of corrosion data, a reliable corrosion prediction based on empirical observations could be challenging. Artificial neural network (ANN) modeling was used for corrosion data pattern recognition by mimicking human neural network systems. Predictive models were developed based on corrosion data acquired in this study. The model was adaptable through iteratively update its prediction by error minimization during the training phase. Trained ANN model can predict uniform corrosion successfully. In addition to ANN, fuzzy curve analysis was utilized to rank the influence of each input (temperature, pH, Cl-, and time). For example, temperature and pH were found to be the most influential parameters to uniform corrosion. This information can provide feedback for ANN improvement, also known as “data pruning”.