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.
S et al. (Thu,) studied this question.