The digitization of manufacturing processes opens up the possibility of data-driven quality predictions based on machine learning (ML) methods, also known as predictive quality. The predictions serve as a decision-making basis for process experts to initiate quality-improving measures in the process at an early stage. Although predictive quality is essentially based on known methods of data processing and ML, there is a need for further research to establish it. On the one hand, there is a lack of implementation-oriented and transferable approaches for the successful realization of predictive quality. On the other hand, key challenges have to be overcome, especially since the collection of representative data in manufacturing processes is costly and the processes are characterized by constant changes. This dissertation addresses these research gaps. First, a generic predictive quality process model called MERLIN is presented, which describes the necessary steps to realize a predictive quality application. Then, two simulation-to-reality transfer learning approaches are presented, which consist of using low-cost data from manufacturing simulations for training predictive quality models. Finally, a novel continual learning method called MAS-Cloning is presented to train an artificial neural network over changes in the manufacturing process. The goal of this method is to maintain predictive accuracy during model updates while improving training and data efficiency.weiterlesen