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Framework integrates AIoT and digital twin technologies, improving efficiency in smart manufacturing, suggesting major implications.
The integration of AIoT technology with DT technologies is the basis of a new industrial revolution in industrial automation and intelligent manufacturing. This study develops a framework for AIoT-based digital twin systems, which combines live IoT data with AI simulation and optimization models. The designed model is based on a four-layer cyber-physical structure including data gathering from the edge, stochastic simulation, state encoding using graph attention networks, and closed-loop execution. The framework was analyzed using 10,000 stochastic simulations and a 12-week industrial experiment in which the system performed schedule performance of 96.8%, OEE of 84.7%, and 16.5% reduction in energy consumption per tonnage produced. The developed multi-objective reinforcement learning algorithm showed an integrated relationship between waste reduction and increased OEE (r = -0.73), with a total OEE improvement of 34.1% due to sustainable processes. The global AI-powered digital twin market is forecasted to grow up to $12 billion by 2030 with 26.2% CAGR.
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