Reinforcement learning-based control improves efficiency and precision in smart manufacturing robotics

Smart manufacturing relies increasingly on automation and robotics to enhance productivity, precision, and operational flexibility. This paper presents an original primary research study detailing the development, implementation, and evaluation of a novel reinforcement learning (RL) model tailored for robotic control in dynamic manufacturing environments. The proposed model improves adaptability, efficiency, and task execution accuracy by leveraging real-time data-driven decision-making. Key contributions include an optimized reward function, adaptive policy updates, and seamless integration with industrial IoT (IIoT) frameworks to enable intelligent and autonomous manufacturing workflows.
The study employs a rigorous experimental framework encompassing extensive training, validation, and comparative benchmarking against traditional control methods such as PID and MPC controllers. Results demonstrate a 30.5% reduction in task completion time, a 41.2% decrease in error rates, and a 20.5% improvement in energy efficiency compared to conventional methods. Furthermore, the RL model enhances scalability by maintaining high performance across varying production scales and improves failure recovery time by reducing downtime by up to 45%. The proposed system also exhibits notable cost savings, reducing operational expenses by 17.6% while extending component longevity by minimizing wear and tear.
These findings validate RL as a transformative solution for industrial automation, offering superior efficiency, robustness, and cost-effectiveness compared to existing control mechanisms. The research provides a scalable and adaptable framework for manufacturers seeking to optimize robotic performance, enhance reliability, and achieve long-term sustainability in Industry 4.0-driven production environments.