Applied AI for Intelligent Die and Tooling Lifecycle Management in Aluminium Manufacturing

The manufacture of aluminium products by extru-sion, die casting and related machining relies on tooling whose condition affects quality, scrap and production continuity. This narrative review examines how applied artificial intelligence can connect design, production, inspection and maintenance into an intelligent tooling lifecycle. It draws on 33 research publications dated 2020–2026, comprising the original 30-publication corpus and three publications added through a targeted update. Separate guidance from the Project Management Institute informs an adaptation of Cognitive Project Management for AI for decision governance. Aluminium-specific research supports selected design and quality-prediction tasks; adjacent machining studies demonstrate sensor-based and image-based wear estimation, remaining-life prediction and digital-twin monitoring. Transfer of these methods to extrusion and die-casting tools requires separate validation. Recent simulation-based extrusion-die optimisation strengthens the design evidence without establishing industrial die-life benefits. Key challenges include sparse damage labels, changing operating conditions, transfer between die families, uncertainty assessment and fragmented lifecycle records. A closed-loop framework links design, virtual qualification, pro-duction sensing, diagnosis, prognosis, maintenance and knowledge capture. The synthesis supports evaluation of human-supervised hybrid intelligence combining physical understanding with traceable data-driven predictions. Governance separates model release, intervention approval and return-to-service authority, with named owners, decision records and reapproval after material changes. The integrated framework remains a proposal; effects on die life, scrap, downtime and decision quality require prospective industrial evaluation.