Dynamic Characterization and Model-Based Design of Metal Scrap Shredders Toward Digital Twin Implementation

Shredder machines are widely used in the recycling industry for the treatment of end-of-life vehicles and other metallic waste. Despite their industrial relevance, their design remains largely empirical because of the inherent complexity of their operation. The rotor is typically composed of disks with many pivoting hammers. It undergoes dynamic and impulsive loads, being highly dependent on several factors as type, size, and configuration of the incoming scrap, the shredder’s speed, and the wear state of hammers, which significantly affects the system dynamics during service. That variability introduces several significant uncertainties, preventing the establishment of a systematic design and scaling methodology for machines dedicated to different scrap streams. This work describes the development of a dynamic model of a hammer-type shredder, aiming at balancing the need for high fidelity of models with computational efficiency and enabling its use in the design optimization and sensitivity analyzes. The proposed model predicts rotor dynamics, hammer kinematics, and variable loading conditions, while remaining lightweight enough for Design of Experiments and repeated simulations. Moreover, the model aims at playing the role of digital twin, to support condition monitoring and predictive maintenance, once validated. This approach to design looks key to increase the reliability and sustainability of shredder’s technology in metal recycling.