Physics-Guided Data-Driven Hybrid Surrogate Modeling Approach for Energy Optimization in Industrial Robotic Grinding

Abstract Optimizing the energy consumption(EC) of industrial robots plays a crucial role in promoting their large-scale application to support the realization of Industry 4.0. Notably, robotic grinding is particularly energy-intensive, attributed to the complex coupling between robot dynamics and the continuous contact forces required for surface finishing. Furthermore, given the intricate nature of the EC process, it is challenging to establish a precise numerical relationship between robotic process parameters and EC relying exclusively on physical mechanisms. And data-driven models require large datasets and often fail to achieve high accuracy under small-sample conditions. To address this, we propose a physics-guided data-driven EC prediction model (EC-PGDD). The heat conduction equation is employed to characterize the underlying relationship between temperature and EC, and a regularization term within the loss function of the temperature prediction model-by embedding differential operators, a smoothness prior is imposed, enhancing extrapolation in sparse data regions. Based on the physical information provided by this equation, EC-PGDD integrates predicted temperature with process parameters, encoding richer information than purely data-driven approaches. To validate the effectiveness of the proposed model and provide targeted strategies for process parameter optimization, Validation experiments on a battery end-plate robotic grinding unit., utilizing the NSGA-II algorithm for multi-objective optimization, demonstrated a 7.67% reduction in energy consumption and a 0.23% reduction in execution time.