Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm

Abstract

Corrosion is an electrochemical reaction that leads to the deterioration of metallic materials, posing significant challenges across various industries. Traditional corrosion analysis methods require manual data collection using electrode sensors and laboratory-based analysis, limiting automation, mobility, and predictive capabilities. To address these issues, a Corrosion Analysis Tool was developed using a Pencil Graphite Electrode Sensor in combination with machine learning algorithms. The tool integrates regression analysis to enhance data integrity, automate predictions, and minimize human errors. Cloud computing is employed to replace traditional physical servers, facilitating remote access and real-time analysis. A mobile application is also developed to provide users with a convenient and efficient corrosion analysis platform. The system was evaluated by comparing its corrosion rate analysis results with traditional laboratory experiments conducted by chemical science students. Results demonstrated high accuracy, with minimal deviations between the corrosion rate values obtained from the Corrosion Analysis Tool and manually computed rates. The differences observed were 0.236 × 10⁻⁸ for a 7-day immersion, 0.049 × 10⁻⁸ for a 14-day immersion, 0.071 × 10⁻⁸ for a 21-day immersion, and 0.014 × 10⁻⁸ for a 28-day immersion, confirming the system\u27s reliability. The precision test further verified that the tool effectively reduces human errors and enhances data integrity. Furthermore, the tool streamlines project management by centralizing data storage and organization, preventing data redundancy and loss. In conclusion, the Corrosion Analysis Tool successfully automates corrosion analysis, improves mobility, and enhances data-driven decision-making for researchers. The system meets all user requirements, offering a robust solution to traditional corrosion analysis challenges. Its predictive capabilities, powered by machine learning, provide valuable insights for future corrosion prevention strategies. By incorporating cloud-based storage and mobile accessibility, the tool modernizes corrosion analysis and contributes to advancements in materials science and engineering