AI-enabled Robotic Multi-machine Operation and Intelligent Production Management System
Keywords:
Artificial intelligence, Industrial robotics, Industry 4.0, Intelligent production management, Multi-machine operation, Smart manufacturingAbstract
In modern manufacturing industries, efficient utilization of resources and reduction of machine idle time are critical for improving productivity. Traditional multi-machine operation systems, whether manual or pre-programmed robotic, often lack adaptability, intelligent decision-making, and real-time production optimization. This study presents an AI-enabled robotic multi-machine operation and intelligent production management system designed to enhance manufacturing efficiency and automation. The proposed system integrates industrial robots with multiple CNC machines and employs artificial intelligence techniques to enable dynamic task scheduling, intelligent machine allocation, and real-time decision-making based on machine status and production requirements. Specifically, a deep reinforcement learning (DRL) model is implemented to learn optimal scheduling and resource allocation strategies from historical and real-time machine data. Machine data, such as cycle time, availability, and operational status, is collected and analysed using AI algorithms to optimize robot movements and production flow. The system aims to minimize machine idle time, improve overall equipment utilization, and reduce human intervention. Experimental results and performance analysis demonstrate that the AI-driven approach significantly improves production efficiency compared to conventional robotic systems. This research highlights the potential of combining artificial intelligence, robotics, and computer engineering to achieve smart manufacturing solutions aligned with Industry 4.0 principles.
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