Development of a Digital Twin for a Bakery Line With Predictive Analytics and Adaptive Control Functions
Amirkhanov B. Kunelbayev M. Amirkhanova G. Nurgazy T. Tyulepberdinova G. Tletay S.
January/December 2026John Wiley and Sons Inc
IET Collaborative Intelligent Manufacturing
2026#8Issue 1
This paper presents the development and validation of a digital twin system for a bakery production line, integrating real-time sensor data, physics-based process models and advanced predictive analytics with CNN + LSTM neural networks. The proposed architecture combines logistic growth, moisture evaporation and heat transfer equations with deep learning for accurate prediction and early detection of baking defects. Simulation and pilot implementation results demonstrate that the digital twin reproduces dough volume dynamics with an error below 3%, predicts humidity within ± 2% and stabilises oven temperature in a narrow range (± 1.2°C). The intelligent system enabled a 77% reduction in unplanned equipment downtime, decreased alarm events by over 60% and reduced the share of defective products from 8% to 2%. These outcomes highlight the practical impact and scalability of the hybrid digital twin framework for improving product quality, minimising losses and enhancing process reliability in food manufacturing.
data analysis , fault diagnosis , intelligent manufacturing systems , neural nets
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Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Institute of Information and Computational Technologies, Almaty, Kazakhstan
Department of Artificial Intelligence and Big Data
Institute of Information and Computational Technologies
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026