Investigation of Microstructure and Stress Corrosion Cracking in Al-6061-T6 Alloy at Different Loads

Stress corrosion cracking (SCC) refers to the damage of mechanical components which are under the combined action of static load and corrosive environment. This phenomenon occurs in various applications including naval and aerospace industry where aluminum and steel alloys experience mechanical loadings in the presence of corrosive environments. In this research work, microstructural and environmental influence on corrosion behavior of Al-6061-T6 at different static loads was investigated. A new test fixture was developed for stress corrosion cracking. Dog-bone shaped tensile specimens of Al-6061-T6 were manufactured using CNC milling machine. Tests were conducted at constant loads of 200 N, 500 N and 800 N, in three different environments: dry ambient conditions, distilled water and 3.5% NaCl solution. Testing continued for different intervals of time i.e. 96 hours, 68 hours and 4.5 hours respectively. After each set of experiments, specimens were observed for cracks using metallurgical microscope. Detailed fractographic investigation of all the tested specimens was carried out using Scanning Electron Microscope (SEM). Excessive corrosion and material degradation was observed in specimens tested in distilled water and 3.5% NaCl environments. Microstructural analysis depicted pitting corrosion and crack deformation.  Some regions clearly showed that grain boundaries were attacked due to oxidation and chemical attack causing weakening of grain boundaries and resulted into intergranular corrosion. Precipitates and grain boundaries in Al-6061-T6 served as a reason of crack initiation due to hydrogen diffusion. Fractographic investigation provided the evidence of trans granular fracture as well as intergranular fracture which was observed as dimples and extensive ductile tearing.

A Novel Feature Selection Technique Using Rough Set Theory

This is era of information. It is common to find the datasets with hundreds and thousands of features used by real world applications. Feature selection is a process to select subsets or features which are more informative. Feature selection technique is used to remove irrelevant and redundant features without losing much of the information. Recently Rough Set Theory (RST) becomes a dominant tool for FS. It is a theory which provides both data structures and methods to perform data analysis. Rough set theory has offered new ideas and trends for the features selection and deal with inconsistent information. Reduction of attribute is an important issue in rough set theory. Many feature selection techniques have been presented in literature using RST. However, majority of these techniques do not ensure optimal feature subsets and suffer serious performance bottlenecks especially in case of large datasets. In this thesis, we modified genetic algorithm to find subset of features within minimum execution time. In its conventional form, genetic algorithm is heuristic based approach; however, using genetic algorithm does not ensure the optimal feature sub selection. In this thesis, we have modified the algorithm such that the resulted feature subsets are not only the optimal but the resulting performance is also improved. The proposed approach was examined with other state of the art FS approaches various publically available datasets at UCI. Results show that efficiency and effectiveness of the proposed approach are better.