# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 3 RMS Barkhausen Noise and Extended Measured Parameters Applied to Different Spacer Thickness  
A series of BN measurements were carried out (1) without a spacer, i.e with direct contact of the magnetizing yoke and sample surface, (2) by applying a 30 \(\upmu\)m thick nonmagnetic spacer, (3) by applying a 40 \(\upmu\)m thick nonmagnetic spacer, (4) by applying a 70 \(\upmu\)m thick nonmagnetic spacer, (5) by applying a 120 \(\upmu\)m thick nonmagnetic spacer, and (6) by applying a 220 \(\upmu\)m thick nonmagnetic spacer between the magnetizing yoke and sample surface. The aim of the work is to investigate whether a correlation can be found between surface roughness and magnetic behaviour, and further evaluate the role of a spacer to reduce the effect of surface roughness by providing a uniform airgap. The evaluation was made through direct experimental results and machine learning paradigms. The following Figures 2-5 look at the varying RMS BN responses for the different materials with increasing surface roughness starting from material 23 to material 28 (see Table 1 and Figure 1 for more information). The four figures display top longitudinal and transversal measurements and the bottom longitudinal and transversal measurements, respectively, with both with or without spacers applied to the measurements. Looking between the different machined parameters (different material IDs) without the use of a spacer (space size: 0), there is no clear distinction in terms of the surface roughness and BN response--except there is a lot of variation present from the returned RMS BN responses. The assumption here is the variation obtained is based on the surface roughness as the material has the same properties for each sample. Figure 3: BN signal response for top surface in longitudinal orientation. Figure 2: BN signal response for top surface in transversal orientation: space test. Figure 4: BN signal response for bottom surface in transversal orientation. Figure 5: BN signal response for bottom surface in longitudinal orientation. Figures 2-5 show the test comparing no spacer (spacer size = 0 on the plots) and then a 30 \(\upmu\)m thick spacer with varying increments of up to 220 \(\upmu\)m. These figures display the effects of using different thicknesses of spacers to suppress the influence of the surface roughness and reduce the variation to improve the overall results. At the point of 220 \(\upmu\)m, this could be considered as the limit for the BN electromagnetic process where the retrieved response is tending towards the levels of background electromagnetic noise (where any BN reading that is within 5 times of the background noise is ignored). The useful role of nonmagnetic spacers for suppressing the effects of varying surface qualities was demonstrated by viewing Figures 2-5. With different levels of surface roughness allowed, a study can be conducted in terms of what are the influencing effects when carrying out Barkhausen noise measurements and the associated decrease in surface quality. This is particularly important when considering the actual in-service conditions, given such differences in the surface quality or oxidation effects from a harsh environment. A number of constants were adopted to understand these phenomena of interest, and, in this case, the surface quality was considered to be most important. The material, the machining process, and the Barkhausen parameters (frequency and voltage magnitude) all remained the same throughout. The experimental work displayed a good correlation between different surface roughness conditions. Figures 6-8 display the extended parameters of BN and were used to increase the data dimensionality to display such effects. These figures display their correlation with the RMS Barkhausen noise signal, as well as provide data to test the effects of the increased data dimensionality applied to the machine learning techniques.