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Kinematic modeling and error compensation are critical for ensuring precision in industrial robots and Articulated Arm Coordinate Measuring Machines (AACMMs). This project investigates the integration of Artificial Intelligence (AI) and Neural Networks directly into the kinematic models of these systems. The core hypothesis is that AI can replace traditional models with more efficient, flexible architectures capable of adapting to unforeseen changes and non-linearities in complex kinematic chains.

Unlike rigid traditional designs, neural networks can manage drifts in error sources and adapt to environmental shifts—provided they are correctly trained—thereby maintaining optimal precision over longer periods and drastically reducing the need for manual recalibration.

The research explores several integration levels: from hybrid models combining neural networks with physical system parameters to fully AI-based kinematic models. To ensure metrological traceability, the project utilizes Digital Twins to generate precise synthetic training data and simulate in-process operating conditions. This approach enhances training quality and model accuracy, even when dealing with incomplete data.

The application of these neural models will streamline collision detection, trajectory generation, and real-time calibration, driving the evolution toward smarter, autonomous manufacturing capable of self-correcting in real time. This innovative approach holds significant potential to transform the field of kinematic equipment calibration.