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This research aims to develop and validate a digital twin system for a bakery production line that enhances process control and defect detection.

Development of a Digital Twin for a Bakery Line With Predictive Analytics and Adaptive Control Functions

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This research aims to develop and validate a digital twin system for a bakery production line that enhances process control and defect detection.
Developed a digital twin system integrating real-time sensor data and process models.
Utilized CNN + LSTM neural networks for predictive analytics.
Executed simulations and pilot implementations to validate system performance.
Achieved dough volume prediction error below 3%.
Predicted humidity within ± 2%.
Stabilized oven temperature within ± 1.2°C.
Reduced unplanned equipment downtime by 77%.
Decreased the share of defective products from 8% to 2%.

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Digital twin technology improves product quality in food manufacturing, highlighting its significant scalability.

Amirkhanov et al. (2026) studied this question.

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