Sustainable Design and Lifecycle Prediction of Crusher Blades Through a Digital Replica-Based Predictive Prototyping Framework and Data-Efficient Machine Learning

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing.