This invention discloses a mold life prediction and maintenance decision-making
system based on digital twins, including modules for
data acquisition, data fusion, digital twin construction and updating,
remaining life prediction, maintenance decision-making and optimization, and closed-loop execution and feedback. The
system collects real-time operating data and production parameters of the physical mold, processes them to generate a comprehensive health status index, and constructs a dynamic digital twin. Based on this digital twin, it simulates future production plans, predicts the remaining lifespan of the mold, and, combined with production scheduling, resource inventory, and cost models, generates and executes the
optimal maintenance decision plan. Through a closed-loop feedback mechanism, the
system continuously updates and optimizes the digital twin using post-maintenance data, achieving dynamic optimization of mold health
status assessment, lifespan prediction, and maintenance strategies. This effectively improves mold management and reduces maintenance costs and production
downtime risks.