Corrosion Behavior of Friction Stir Welded AA8090-T87 Aluminum Alloy

Shyamlal, C.; Shanmugavel, Rajesh; Jappes, J. T. Winowlin; Nair, Anish; Ravichandran, M.; Abuthakeer, S. Syath; Prakash, Chander; Dixit, Saurav; Vatin, Nikolai

doi:10.3390/ma15155165

Cited by 22 publications

(2 citation statements)

References 28 publications

(27 reference statements)

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“…The capacity of fuzzy logic to manage uncertainty makes it a viable tool for improving energy management techniques in the presence of changeable renewable supplies. [26][27][28][29][30] Performance evaluation and comparative studies have been conducted in the literature to assess the effectiveness of energy management systems based on fuzzy logic. These assessments often include indicators such as grid stability, dependability, and overall efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Sharma,

Raju,

Dhall

et al. 2024

E3S Web Conf.

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This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Sharma,

Raju,

Dhall

et al. 2024

E3S Web Conf.

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“…Depending on the SEM data, each specimen demonstrates that the corrosion type which occurs is pitting corrosion, which would be characterized by holes. The formation of holes in the corroded area indicates the level of surface degradation [36].…”

Section: Sem Testingmentioning

confidence: 99%

Morphology Study of the Corrosion Rate on Weld Joint of Double Side Friction Stir Welding Aluminum Alloy AA6061

Crisdion

Puspitasari

Permanasari

et al. 2023

jtmt

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DSFSW welding uses the rotating of a tool in the workpiece to create heat owing to friction between workpiece and tool to be connected. Corrosion is influenced by the presence of high heat owing to friction and a non-uniform cooling rate. The heat transforms the metal in the Heat Affected Zone (HAZ) and Weld Metal (WM) region, which can result in flaws such porosity, kissing bond, fractures, voids (from welding penetration), and flash as well as changes to the microstructure. Changes in the microstructure of welded joints can affect the resistance of welded joints to corrosion. The purpose of this study is to experimentally analyze the main causes of failure of AA6061 DSFSW joints based on welding temperature, weld defects, microstructure, corrosion rate and morphology of the corroded surface. Temperature testing using thermocouple to analyze the temperature, welding joint defects using DSLR camera and radiography test, microstructure using optical microscope, corrosion rate using AUTOLAB PGSTAT and morphology of corroded surface using SEM. The temperature analysis results show that the advancing side has a higher temperature than the retreating side, due to friction between the tool and base metal accompanied by the opposite welding direction. Visual inspection shows that all specimens and welding positions produce flash that is quite rough on the top (1G) and bottom (4G) surfaces and radiographic test results show incomplete fusion in 4 specimens. Microstructure shows a change in shape and size resulting in recrystallization in the form of fine grains. The highest corrosion rate is found in specimen B 1G welding position of 0.63856mm/year and the lowest corrosion rate in specimen A of 0.058567mm/year. SEM test results show the type of corrosion that occurs in DSFSW welding joints is pitting corrosion.

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