IoT-Enabled Cognitive Manufacturing Systems: A Conceptual Framework for Real-Time Autonomous Decision-Making in Industry 4.0

Abstract

The accelerated evolution of Industry 4.0 has further accelerated the adoption of the Internet of Things technology in manufacturing infrastructure, which enables real-time data collection and improved operational transparency. However, smart manufacturing infrastructure currently relies on human intervention and predetermined decision algorithms, which limit its ability to adapt to complex and dynamic manufacturing environments. This study aims to address this limitation by presenting a conceptual framework for an IoT-based cognitive manufacturing infrastructure that enables real-time autonomous decision-making. This infrastructure will leverage IoT-based sensing technology, edge computing technology to process data in real-time, and cognitive intelligence technology based on artificial intelligence and machine learning algorithms. The proposed architecture utilizes a multi-layer structure that includes data acquisition, processing, cognitive analytics, decision-making, and execution layers with the help of continuous feedback and learning mechanisms. This allows the manufacturing systems to sense the environment and process the data to optimize the processes with minimal human involvement. The study also presents the applications of the proposed framework, such as predictive maintenance, real-time quality management, production optimization, and energy management, which show the flexibility and applicability of the proposed framework for smart manufacturing systems. The paper also covers the challenges faced by the proposed architecture and the future scope for the proposed framework. The proposed framework provides a flexible and scalable platform for the development of intelligent and self-optimizing manufacturing systems and hence contributes to the development of next-generation manufacturing systems.