Multi-Criteria Framework for Evaluating Robotic Arm Power Prediction Models

As the use of industrial robotic arms (RAs) increases, effective energy management has become a critical requirement for manufacturing competitiveness and sustainability. However, existing power prediction models are often based on complex kinematic or dynamic formulations, limiting their applicability on the shop floor. To address this challenge, this study develops an evaluation framework for regression-based RA power prediction models that integrates accuracy, explainability, and practical considerations. Specifically, 162 statistical and machine-learning models are evaluated in terms of model type, movement type, training data size, and training time. The results show that the support vector machine (SVM) consistently outperforms other models in both accuracy and computational efficiency, while the multilayer perceptron (MLP) performs the worst. Using Shapley additive explanations (SHAP), the framework also clarifies how the most effective models capture the physical characteristics of RA movements embedded in power data. Moreover, the analysis reveals that similar movement patterns, such as along the X and Y axes, can result in distinct power demands. These findings highlight the need for explainable and practical prediction models to support energy-efficient RA operations and provide shop-floor engineers with actionable insights into the physical mechanisms driving power demand.