Material Parameter Dataset for Robot Manipulator Stability Prediction Using Hybrid Neural Networks

This dataset contains 1,728 samples of material parameters for industrial robot manipulator stability prediction. Each sample includes stiffness (N\/m), damping (Ns\/m), deflection (mm), and a computed stability index value. The dataset covers four widely-used industrial manipulators: ABB, FANUC, KUKA, and UR5. Input parameters for the calculations were extracted from manufacturer product brochures and technical specifications, including arm length, mass, weight, Young's modulus, material composition, inner diameter, outer diameter, and thickness. Material parameters were systematically computed for various manipulator configurations to capture the influence of structural and material properties on robotic stability. The stability index is calculated as a function of stiffness, damping, and deflection, providing a quantitative measure of manipulator performance. This dataset is designed for training and validating hybrid neural networks and machine learning models that can learn to predict stability or classify stability levels (low, medium, high) based on material properties. Data is provided in CSV format with clearly labeled columns and units for immediate use in neural network training workflows.