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The purpose of this study is to refine the microstructure and improve the corrosion behaviour of aluminium alloy AA5083 by subjecting it to friction stir processing (FSP).

FSP trials are conducted as per central composite design, by varying tool rotation speed, tool traverse speed and shoulder diameter at three levels. The microstructure is examined and the hardness is measured for both the base material and the processed workpieces. The corrosion behaviour of the base material and processed workpieces is studied using potentiodynamic polarization technique for three different testing temperatures, and the corrosion current and corrosion rate are calculated.

The results reveal that FSP refined the grains, dispersed secondary phases, increased the hardness and improved the corrosion resistance of most of the friction stir processed specimens than the base material at all the three testing temperatures. Grain refinement and fine dispersion of ß phase improves the hardness and corrosion resistance of most of the FSPed specimens. However partial dissolution of ß phase decreases the hardness in some of the specimens. Most of the FSPed specimens displayed more positive potential than the base material at all the testing temperatures representing a higher nobility than the base material, as a result of fine dispersion of secondary phase particles in the matrix. Large pits formed on the surface of the base specimen indicating a higher corrosion rate at all three testing temperatures. The SEM image of FSPed specimens reveals the occurrence of very few pits and minimal corrosion products on the surface, which indicates lower corrosion rate.

The corrosion mechanism of the friction stir-processed AA5083 specimens is found to be a combination of activation and concentration polarization.

The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.

The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.

This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.

The conventional real time issues such as nonlinearity characteristics, poor controllability and stability.

It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.