Leveraging Artificial Intelligence for Real-Time Production Optimization in Smart Manufacturing Systems

Abstract

In the era of Industry 4.0, artificial intelligence (AI) technology is playing an increasingly important role in optimizing production processes within innovative manufacturing systems. The use of AI enables real-time data analysis, which helps to improve efficiency, reduce operational costs, and improve product quality. However, the application of AI in production optimization still faces various challenges, such as integration with existing production systems and the need for skilled workforce training. This research aims to explore the application of artificial intelligence technology for real-time production optimization in intelligent manufacturing systems. The primary focus of this research is on applying machine learning techniques to predict and manage dynamic production variables. This study employs an experimental approach, implementing machine learning algorithms, including artificial neural networks and reinforcement learning, to process real-time production data. The system is tested in imaginative manufacturing scenarios, by monitoring key production parameters and adjusting the process based on the predictions generated by the AI model. The results of the experiment demonstrate that the application of AI in real-time production optimization can enhance production efficiency by up to 20%, reduce energy waste, and improve product quality. The system also demonstrates the ability to adapt quickly to changing market and production conditions.