This research describes the development of an Artificial Intelligence Based Manufacturing Optimization System that employs state-of-the-art Artificial Intelligence resources such as LangChain framework, Groq\\\'s LLaMA-3.3-70B Large Language Model and Streamlit interface to transform and optimize how factories work. The system provides real-time analytics, predictive maintenance algorithms and Intelligent Manufacturing Process Optimization to increase production efficiency, lower operational costs, and minimize waste of materials in manufacturing. To create these optimized processes, the core system uses the LLaMA-3.3-70B model and the power of Generative AI technology to process all types of complex manufacturing data such as machine specifications; material characteristics; manufacturing schedules; quality metrics; resource limitations; etc., into optimized workflows for manufacturing operations. The system uses ChatPromptTemplate to dynamically adjust to the type and context of manufacturing so any company using this software will receive customized recommendations for using the correct processes to manufacture products, whether 3D printing, CAD, or Production Planning. The system uses a sophisticated Cost-Benefits Analysis Engine to calculate for each optimization scenario the cost of manufacturing (operations), materials, energy, and the total amount of money that can be saved through these optimized processes. The Streamlit framework allows industrial engineers in the manufacturing environment to easily enter their parameters, select production process optimization goals, and receive AI-Derived optimized recommendations, including in-depth analytics supporting those recommendations. Another feature of the system is its ability to provide Real-Time Monitoring of key Performance Indicators (KPIs) such as Overall Equipment Effectiveness (OEE), production defects, cycle times and so on.
Panduri et al. (Thu,) studied this question.