Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges

In recent years, the integration of intelligent industrial process monitoring, quality prediction, and predictive maintenance solutions has garnered significant attention, driven by rapid advancements in digitalization, data analytics, and machine learning. As traditional production systems evolve into self-aware and self-learning configurations, capable of autonomously adapting to dynamic environmental and production conditions, the significance of reinforcement learning becomes increasingly apparent. This paper provides an overview of reinforcement learning developments and applications in the manufacturing industry. Various sectors within manufacturing, including robot automation, welding processes, the semiconductor industry, injection molding, metal forming, milling processes, and the power industry, are explored for instances of reinforcement learning application. The analysis focuses on application types, problem modeling, training algorithms, validation methods, and deployment statuses. Key benefits of reinforcement learning in these applications are identified. Particular emphasis is placed on elucidating the primary obstacles impeding the adoption and implementation of reinforcement learning technology in industrial settings, such as model complexity, accessibility to simulation environments, safety deployment constraints, and model interpretability. The paper concludes by proposing potential alternatives and avenues for future research to address these challenges, including improving sample efficiency and bridging the simulation-to-reality gap.

搜集汇总

背景与挑战

背景概述

该数据集是一篇关于强化学习在工业4.0中自主过程控制应用的综述论文，发布于2024年8月5日，作者为Nuria Nievas等人。论文总结了强化学习在制造业（如机器人自动化、焊接、半导体等）的应用实例，并分析了关键优势（如自适应能力）和主要挑战（如模型复杂性、安全部署约束）。