# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 1 Introduction  
Magnetisation processes are closely related to microstructures, such as domain walls in crystalline solids where domain walls pinning can provide the detection of defects [1; 2]. Therefore, magnetic measurements can be successfully applied for the characterization of structural changes in ferromagnetic materials. Due to its non-invasive approach, magnetic testing is used to carry out non-destructive testing, where measurements can be inferred in terms of the detection and characterization of defects or structural degradation within ferromagnetic materials. Such applications are very palatable to safety critical applications where destructive testing, especially in-service, is not a viable option--for example, the structural integrity of nuclear power systems such as reactor pressure vessels. An offshoot of micromagnetic methods is that of Magnetic Barkhausen noise (MBN), and this technique is central to the work presented here. A Non-Destructive Evaluation provided by MBN has been found to be a useful technique for the examination of surface defects caused from manufacturing or microstructure change, and the residual stresses often caused by manufacturing parameters [3; 4; 5]. The principle behind MBN is when a continuously changing electromagnetic exciting field (H) isapplied to a material and a response in the form of discontinuous changes in magnetisation (M) occurs when the domain walls encounter pinning sites. Such pinning sites are in the form of dislocations, precipitates in the matrix, grain boundaries, or small volumes of second-phase materials. During the electromagnetic hysteresis cycle, such pinning sites prevent the movement of the domain walls within the material, which is the premise behind the Barkhausen noise phenomena [6]. Energising the local material area electromagnetically is carried out from both magnetising and sensing via a soft ferrous yoke and an exciting/pickup coil, respectively. The yoke is in contact with the workpiece material under testing, therefore the magnetic coupling is established. The surface quality, especially in service conditions of safety critical structures, can vary from smooth to very rough. These differences impact the quality of the magnetic coupling between the yoke and specimen surface, and, in severe cases, can influence the mechanism of excitation as well as the sensing to give totally misleading responses. This is a well-known problem with magnetic sensing technologies [7]. To mitigate these effects, the application of a spacer between the yoke and sample is used. This may reduce the effects of surface quality, however the spacer substantially decreases and distorts the measured signal and thus the measurement of basic magnetic parameters of the sample material can be very difficult or even impossible. Bearing the previous statements in mind, spacers are still applicable for magnetic measurement of the relative structural differences found within ferromagnetic materials. This is especially true if the measurement is carried out by analysing the measured signal correlated to the material permeability as displayed in [8]. The Barkhausen noise is based around a similar process of [8], albeit a local point space area is used for inspection as opposed to the bulk material and a significantly larger area. For safety critical components, it is important to characterise the presence of any surface defects including cracks, manufacturing flaws, service-induced cracking, or suspected degradation as these defects can initiate and grow during service and may cause catastrophic failure by fracture. Hence, most of the structural integrity assessment methodologies tend to be highly conservative. By accurately predicting the surface quality, it is possible to obtain more reliable NDT measurements, since this creates an opportunity to feed back this information to the signal evaluation and to mitigate this side effect as well.