Supervised learning can be employed to predict product quality, enabling the optimization of parameter configurations before and during the manufacturing process. This approach helps to reduce material and resource waste. The focus of this research is the cold ring rolling process. In this context, process parameters serve as features for the machine learning (ML) algorithm, while form errors are used as labels. However, a sufficiently large database is necessary. 3D FE simulation presents a more cost-effective and efficient method for generating substantial amounts compared to experiments. Both from experiments and simulations, point cloud files of final rings can be obtained, which are essential for the comparative calculation of form errors. A novel method is developed to calculate form errors based on point cloud files. The calculated form errors enable the ML-based quality prediction as labels for supervised ML. The accuracy of the algorithms used for this calculation is subsequently verified, ensuring the reliability of the ML predictions.