Industry 4.0 is growing quickly, which has changed smart production by encouraging the use of real-time tracking, machine learning, and AI-driven systems to make operations run more smoothly. The main focus of this dissertation is on creating and testing an intelligent production system for XRIT that solves important problems like energy management, predictive maintenance, and AI-powered decision support. Machine learning models are built into the system, such as the Random Forest Classifier for proactive maintenance and the Isolation Forest for finding outliers. These models help with decision-making and reducing downtime. Streamlit makes real-time data visualisation possible, giving workers access to dashboards that they can interact with and see real-time observations.The system was tested with fake data and is made to be scalable, so it can be used in real time in XRIT's production setting. Adding an AI-powered virtual assistant made with GPT-4 lets workers get real-time, useful information that makes complicated questions easier to answer and improves operational decisions. The testing shows that the system makes working efficiency, energy management, and the ability to plan repairs much better. Moving the system to real-time data merging and looking for other ways to make it better will be the main focus of future work.
Vallidevi Krishnamurthy (Fri,) studied this question.