Advancements in Smart Manufacturing and Computational Frameworks for Next-Generation Aerospace and Industrial Systems
DOI:
https://doi.org/10.60087/jklst.vol5.n3.004关键词:
Smart manufacturing, Digital twins, Artificial intelligence, Model based systems engineering, Aerospace systems摘要
Smart manufacturing is revolutionizing aerospace and industrial systems by combining artificial intelligence, machine learning, digital twins, the Industrial Internet of Things, model-based systems engineering, advanced analytics, and computational optimization. This review explores recent advancements in these technologies and how they are contributing as a whole to making manufacturing systems intelligent, connected, and resilient. Special focus is placed on interoperable computational architectures based on ISA 95, OPC UA, MQTT and STEP based product information standards, which are well suited for continuous information exchange between engineering, production, quality, maintenance and enterprise systems. The use of AI and machine learning: real-time sensing, automated quality management, predictive maintenance, defect detection and adaptive process control; and operations research and constraint based optimization: production scheduling, resource allocation, capacity planning, multi objective decision making. Digital twins and digital threads now extend the connection between physical assets and engineering models and operational data, providing traceability and continuous improvement throughout the asset's lifecycle. The review also takes into account the human factors, workforce enablement, cyber security, regulatory compliance and governance as key elements of trustworthy smart manufacturing. Issues that are faced include interoperability issues, proprietary formats, lack of digital twin verification and validation, model drift, cybersecurity concerns, computational requirements, and the inability to keep the models up to date in a variable operating environment. The proposed future directions are reduced order and surrogate modelling, physics informed and generative AI, adaptive sensing, Bayesian data assimilation, semantic knowledge graphs and distributed digital twin architectures.
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