Additive-subtractive component manufacturing, i.e., the combination of additive manufacturing and subsequent subtractive mechanical post-processing of the components, places the highest demands on the control and mutual coordination of the individual process steps with regard to the manufacture of highly stressed components (e.g., in aerospace, automotive or medical technology) and the generation of their quality characteristics and functional properties. Many effects within the processes and in particular interactions (e.g. effect of component distortion on allowance, process forces and resulting surfaces, e.g. temperature control, microstructure generation or influence and subsequent component strength) have not yet been fully understood and currently elude comprehensive observation, control and process regulation due to a lack of metrological solutions and digital description approaches. Here, there is particular potential for the use of artificial intelligence and machine learning methods, as well as targeted and adapted software engineering. The goal should be to be able to describe the relationships in such a way that observation and adaptation of the processes and the process chain is made possible in such a way that the required properties (e.g. tolerances, surface and edge zone characteristics, strength, etc.) of the manufactured components are achieved with maximum process reliability. This requires the linking of several learning domains, e.g. by means of transfer learning. For this purpose, an explainable (XAI) method is to be designed and implemented.