## 1 Introduction  
The functionality and safety of power plants require structural materials that fulfill requirements such as high-temperature strength (at operating temperatures of 500 \({}^{\circ}\)C to 650 \({}^{\circ}\)C), sufficient corrosion resistance or resistance to neutron irradiation. Ferritic-martensitic 9-12% Cr steels, such as P91, fulfill these requirements and are widely used for components like main steam pipes, boiler headers and superheater pipes in power plants [1]. Their mechanical strength is derived from the martensitic structure, characterized by numerous strengthening block, lath and subgrain boundaries; the high dislocation density and the strengthening precipitates (M\({}_{23}\)C\({}_{6}\) carbides and MX carbonitrides) [2; 3]. However, given the limited corrosion resistance of 9-12% Cr steels at temperatures up to 620 \({}^{\circ}\)C, high-chromium ferritic steels have been developed as promising alternatives. These steels combine excellent corrosion resistance, which is caused by the relatively high Cr content, with a high mechanical strength at these temperatures due to intermetallic (Fe,Cr,Si)\({}_{2}\)(Nb,W) Laves phase strengthening [4; 5; 6]. In view of the challenges associated with the safety assessment of power plants, the early and non-destructive detection of micro- and macrostructural changes induced by high temperature loadings is important. For ferromagnetic materials, microstructural changes caused by high temperature loading and the resulting changes in mechanical properties can be detected non-destructively, as shown in [7]. In the context of non-destructive evaluation (NDE), magnetic methods are of particular significance due to the close analogy between the modification of the steel's microstructure and the movement of the magnetic domain walls under an applied magnetizing field [8; 9; 10]. In ferromagnetic materials, the movement of dislocations and the movement of domain walls are affected by microstructural imperfections encountered during movement through the material. The resulting correlation between mechanical and magnetic properties is well interpreted, and magnetic methods are usually simple and not expensive. Moreover, in the context of the increasing digitalization and traceability of material properties, non-destructive tools supported by artificial intelligence (AI) methods can improve the characterization of the material properties and their changes along the product life cycle [11]. The application of machine learning (ML) methods in the analysis of non-destructively acquired data offers significant advantages to industries reliant on NDE techniques [11]. By using ML, deep learning and data fusion, AI can enhance the accuracy, efficiency and decision-making of NDE assessments. As technology continues to advance, the integration of AI in NDE undoubtedly plays a crucial role in ensuring safety and reliability across various industries. However, the development of ML-supported non-destructive testing methods requires a sound knowledge of the relationship between the data obtained by NDE, the material degradation caused by typical operation conditions and the underlying macro- and microstructural changes. Therefore, the aim of the research project presented here is to analyze and predict changes in the microstructure and the mechanical properties (e.g., changes in hardness) of the martensitic 9-12% Cr steel P91 and the fully ferritic 17% Cr steel HiperFer-17 Cr2. The investigation focuses on the isothermal cyclic loading in the high-cycle fatigue (HCF) and low-cycle fatigue (LCF) regime at temperatures ranging from 600 \({}^{\circ}\)C to 650 \({}^{\circ}\)C.  
The analysis is based on non-destructive micromagnetic feature measurements, providing critical data and methods for assessing the safety of new reactor designs and for their monitoring during operation. The primary objective of this research is to identify load-induced changes using micromagnetic sensors.