Guest Editorial Special Issue on Learning Theories and Methods With Application to Digitized Process Manufacturing

• Published in: IEEE transaction on neural networks and learning systems 2024-03, Vol.35 (3), p.2914-2916
• Main Authors: Qian, Feng, Jin, Yaochu, Yu, Xinghuo, Tang, Yang, Marin, Guy B.
• Format: Article
• Language: English
• Subjects: Agile manufacturing; Algorithms; Artificial intelligence; Autonomous systems; Cryptography; Decision making; Decision theory; Digital systems; Digital twins; Digitization; Information management; Intelligent systems; Internet of Things; Knowledge management; Learning; Learning systems; Machine learning; Manufacturing; Manufacturing processes; Optimization; Special issues and sections; Supply chains
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2024.3362091

The digitization of process manufacturing involves converting information and knowledge into a digital format through technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. This transformation promotes extension and optimization within the industrial, supply, and value chains, aiming to enhance decision-making efficiency, enable agile operations, and ensure information security and privacy. However, the current learning and operational approaches in the process industry remain rooted in traditional informatization, falling short of the vision for digital transformation. To address this gap, it is crucial to implement fusion analysis, deepen understanding, adopt autonomous learning, and enable intelligent optimization based on life-cycle data. Therefore, it is of fundamental importance to realize the transformation of process manufacturing toward digitalization and intelligentization, i.e., the use of artificial intelligence with decision-making capability, via new learning theories, methods, and algorithms.