In modern markets, increasing quality requirements require high performance quality assurance processes which guarantee the fulfillment of these requirements sustainably. The quality assurance must therefore be able to take targeted countermeasures in the event of deviations. It is becoming increasingly decisive to achieve quality with minimum resource expenditures, increasing the importance of quality control loops. Currently, however, a great deal of manual effort goes into the design and implementation of the mostly knowledge-based control logics due to the heterogeneous data landscape and the resulting data preparation processes. Autonomous quality control loops represent a new development and are intended to provide an efficient and data-based approach to setting up quality control loops. According to the "plug and play"principle, the control system should be operational with a minimum of resources in order to enable precision engineering. Prerequisites for such autonomous systems are homogeneous data structures and models for the holistic representation of quality data, which make individual data preparation processes obsolete. In addition, individual process models must also be replaced by suitable data-based, learning modeling methods. In the following approach, the fundament for a holistic quality data model is developed on the basis of various interviews with diverse companies active in the field of metal-cutting and additive manufacturing. The data model is represented using the Asset Administration Standard of the I4.0 platform. In addition, machine learning approaches in the area of machining and additive manufacturing are analyzed for the general modeling of the correlation between process parameters and the quality result, in order to be able to develop a holistic concept for autonomous quality control on this basis in the next step.