Dear Colleagues,

In recent years, integrating AI and advanced control techniques with manufacturing processes has brought significant transformation to the industry. The ML and control-based schemes have the potential to greatly enhance the performance and robustness of the manufacturing process by actively adapting variations and disturbances while optimizing complex nonlinear systems with data-driven methods. Therefore, this special issue calls for innovative work at the intersection of advanced ML, RL, PHM and control systems in manufacturing processes and industrial applications. We seek to explore the integration of learning-based algorithms to enhance the control and optimization of manufacturing systems, enabling them to adapt to material variations and process disturbances effectively. Submissions should address the challenge of creating transparent, data-driven models and control architectures to support real-time adaptation and resilience in manufacturing.

·Advanced machine learning models for predictive maintenance, fault detection, and quality control in manufacturing.

·Reinforcement learning for adaptive control systems and real-time optimization of manufacturing processes.

·Integration of intelligent control theory and manufacturing application to enhance system resilience and robustness.

·Data-driven optimization techniques for inventory management, production planning, and supply chain logistics.

·Sustainable manufacturing practices enabled by AI-driven process optimization for energy and material conservation.      

·Strategies for improving transparency and trustworthiness in intelligent manufacturing applications.

We look forward to receiving your contributions.

Dr. Zezhi Tang

Section Editor