Integrated Machine Learning and Smart Infrastructure Frameworks for Advanced Additive and Hybrid Manufacturing Systems

Abstract

Machine learning, digital twins, cyber-physical systems, and smart infrastructure are changing the way additive and hybrid manufacturing goes from static, process-defined to adaptive, data-driven manufacturing. The reviewed integrated architectural approach is able to link manufacturing at the physical level with the sensing, data infrastructure, physics-based modelling, surrogate modelling, artificial intelligence, and closed-loop control levels. Digital threads enable ongoing data connectivity and traceability from design to production, inspection, and maintenance phases, and digital twins maintain a dynamic representation of changing conditions in the processes. In the manufacturing sector, Edge and cloud infrastructure make it possible to capture and process data in real time and manage and analyze it at scale in a variety of factory conditions. Surrogate and physics-informed models complement high-fidelity physics-based simulations for reducing computational demands and enabling rapid prediction and optimization. Layer-to-layer and within-layer control strategies further allow the adjustment of manufacturing parameters in an adaptive way using real-time process information. The framework also introduces the possibility of hybrid manufacturing processes: Additive deposition and subtractive machining, finishing, and inspection processes are linked through continuous digital data exchange. Key needs for safe industrial deployment are identified to include safety, cyber security, regulatory compliance, data governance, and model traceability. Overall, the integrated approach offers a way to more autonomous, responsive, traceable, and efficient manufacturing systems, and puts the emphasis on the remaining need for physics-based knowledge, transparent decision making, human supervision, and validated control architectures.