Intelligent Control Systems for Industrial Robots in Manufacturing Processes
Keywords:
Industrial Robotics Intelligent Control Systems Manufacturing Automation Machine Learning Algorithms Neural Network Applications Adaptive Control Systems Fuzzy Logic Control Robotic Process Automation (RPA) Robotics and IoT Integration Digital Twin Applications.Abstract
This paper delves into the advancement of intelligent control systems for industrial robots, highlighting their critical role in optimizing manufacturing processes. By incorporating sophisticated algorithms and control methodologies, such as machine learning, neural networks, and adaptive control, without explicitly relying on the broader concept of artificial intelligence, these systems significantly enhance the efficiency, adaptability, and operational capabilities of industrial robots. Through an extensive review of contemporary technologies and methods, the study showcases the diverse applications of intelligent control systems in manufacturing tasks, including assembly, welding, painting, and inspection. The challenges associated with developing and integrating these advanced control systems—ranging from system complexity and safety to cost implications—are thoroughly examined, alongside a discussion of potential solutions and ongoing research efforts aimed at addressing these issues. Additionally, the paper assesses the impact of these control systems on manufacturing efficiency, product quality, flexibility, and worker safety. It also explores future directions in the development of control technologies for industrial robotics, highlighting the integration of emerging technologies such as the Internet of Things (IoT) and digital twins, without delving into artificial intelligence. The findings emphasize the transformative potential of intelligent control systems in advancing the field of industrial robotics and underscore the necessity for continued research in this domain.
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