This paper introduces an extended outline of the AI-based decision-making in manufacturing facilities with the application of real-time sensor data, machine learning, and adaptive control and puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
Abstract
Abstract. Introduction of Artificial Intelligence (AI) to smart manufacturing has transformed conventional production systems as it allows optimization of processes in real-time. In this paper, the author introduces an extended outline of the AI-based decision-making in manufacturing facilities with the application of real-time sensor data, machine learning, and adaptive control. The offered system will help improve productivity, minimize the downtime, and optimize the product quality with the help of predictive analytics and dynamic optimization. It is experimentally proven that the efficiency, accuracy and operational performance greatly improve when using industrial datasets. The paper puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
An AI-enabled process optimization framework that combines real-time sensor data, predictive analytics, machine learning, reinforcement learning, optimization algorithms, and closed-loop feedback to improve manufacturing performance is proposed.
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