Predictive maintenance using Machine Learning - Capstone project
I spearheaded this project and also was responsible for the following - idea, methodology, Staff coordination & collaboration and AI chatbot development. This project study introduces an advanced predictive maintenance methodology employing machine learning techniques to optimize the reliability and efficiency of the KDF 600i sputter machine. Emphasizing power fluctuation as a critical precursor to failure, the proposed framework meticulously addresses issues stemming from voltage deviations, spikes, and noise, mitigating process disparities, deterioration in product quality, equipment damage risks, and subsequent production downtime. The project encompasses persistent power quality monitoring, systematic assessment of improvement interventions, and seamless integration with a real-time visualization display model to facilitate enhanced operational oversight. Capstone PDF