ADAPTIVE PROCESS MANAGEMENT USING DEEP LEARNING ON A PROGRAMMABLE LOGIC CONTROLLER (PLC)

Abstract

In the era of Industry 4.0, the integration of advanced digital technologies into manufacturing processes has become paramount for enhancing operational efficiency and adaptability. This study introduces a groundbreaking approach to adaptive process management through the integration of deep learning algorithms within Programmable Logic Controllers (PLCs), thus addressing the limitations of traditional PLCs in dynamically adjusting to new operational conditions without manual intervention. By leveraging the inherent capabilities of deep learning for realtime data analysis and decision-making, this research develops a novel framework that enables PLCs to autonomously learn from process data, adapt control strategies in real-time, and optimize manufacturing operations. The methodology encompasses the design and implementation of deep learning models tailored for PLC environments, the development of a data-driven learning mechanism directly on the PLC, and a comprehensive evaluation of the system’s adaptability, efficiency, and performance in real-world industrial settings. The findings reveal significant improvements in process efficiency, reduction in downtime, and enhanced adaptability to changing operational conditions, demonstrating the potential of combining deep learning with PLC-based systems for fostering intelligent and flexible manufacturing processes. This study not only provides a viable solution to the challenges of static PLC programming but also opens new ways for research and development in smart manufacturing technologies, offering insights into the practical implications of deploying intelligent automation systems in Industry 4.0.