A CFD-driven digital twin for automated mold sandblasting in digital manufacturing

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Abstract
The manufacturing industry is facing increasing pressure to improve efficiency and sustainability in response to energy constraints and environmental regulations. Within this context mold cleaning represents a critical and energy-intensive step in resin-based component manufacturing. Although conventional manual sandblasting is widely adopted it is affected by poor repeatability limited process optimization and significant material and energy waste. The objective of this work is to develop and validate a predictive numerical tool capable of supporting the optimization and digitalization of the sandblasting process enabling higher efficiency and improved sustainability. A validated multiphase Computational Fluid Dynamics (CFD) model based on the Discrete Phase Model (DPM) was developed to simulate particle-laden jets interacting with mold surfaces. The methodology involved an accurate characterization of the nozzle flow field to determine particle velocity and distribution at the nozzle exit. The CFD model was rigorously validated through comparison with experimental sandblasting tests evaluating residual material on the mold surface under different nozzle–mold distances and jet inclination angles. Furthermore, a simplified plate-based CFD model was employed to derive optimal nozzle trajectories and pass strategies reducing computational cost while preserving predictive capability. The numerical framework was designed to be integrated into a digital manufacturing environment for a real-time camera-based monitoring. The results demonstrate a strong agreement between numerical predictions and experimental measurements confirming the reliability of the CFD model in describing material removal mechanisms. The proposed approach enables the rational optimization of process parameters and nozzle paths leading to a significant improvement in cleaning efficiency and process repeatability. By reducing unnecessary sand usage process time and rework the methodology contributes to lower energy consumption and material waste. This work introduces an innovative digital tool for sandblasting process optimization supporting the transition toward smarter and more sustainable mold maintenance operations.
Funding Information
  • Università degli Studi di Genova

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