Design and Implementation of ML-based Monitoring System for Detecting Quality Degradation Factors in Battery Factories

-Battery factories play a crucial role in meeting the growing demand for energy storage solutions. However, it is essential that such factories maintain high-quality production standards in their battery manufacturing processes to ensure reliable and safe performance. This thesis focuses on the design and implementation of a machine learning-based monitoring system built to detect quality degradation factors in battery factories. The monitoring system proposed in this research leverages the power of machine learning techniques to identify and analyze factors that can potentially contribute to quality degradation in battery production. By continuously monitoring various parameters and variables throughout the manufacturing process, this system can effectively detect deviations and anomalies that may indicate the presence of quality issues.