# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 2 Surface Quality and Barkhausen Noise Sensor Experiment Setup
### Barkhausen Noise  
From the generated MBN measurements, other parameters are calculated and provide further discriminating quantities, which are used to further discriminate the material conditions. The MBN is an \(RMS\) magneto-elastic parameter (mp) that is expressed as a function of the magnetizing voltage, current, and frequency applied to the material under testing. A time window of several bursts would indicate a general response. The RMS of the MBN response signal is given by Equation (1):  
\[RMS=\sqrt{\frac{\sum_{i=1}^{n}{y_{i}}^{2}}{n}} \tag{1}\]  
where \(n\) is the total number of MBN signal bursts captured within a specified frequency range and \(y_{i}\) is the amplitude of the individual bursts.  
Within the Rollscan 350 Analyser system [29] there are a number of parameters that need to be set for the stimulated electromagnetic waveform. Such parameters are voltage and frequency, as well as a high frequency pickup response filter. Taking advantage of the skin effect equation (see Equation (2) for more information), the lower the frequency, the greater the depth of penetration into the material. Both the magnetising voltage and frequency sweeps were undertaken to identify the optimum parameters based on the displayed material response. A penetration depth between 0.01 mm and 1 mm is achieved by using the frequency pickup filter of 70 kHz to 200 kHz. Using the Microscan software, repeated measurements were then recorded for 5 s of duration [30]. As displayed in other works, [31] describes the used Barkhausen parameters in greater detail. For replication, 2 V and 120 Hz were used as the exciting signal parameters for this surface quality study. This selection was based on previous tests investigating the detection of radiation-induced embrittlement, as well as being in good agreement with the frequency and voltage sweeps of the tested materials.  
\[\delta=\frac{1}{\sqrt{\pi f\mu\sigma}} \tag{2}\]  
where \(\delta\) is the skin depth, \(f\) is the frequency in Hz, \(\mu\) = permeability of sample, \(\mu\) = \(\mu_{0}\)\(\times\)\(\mu_{\mathrm{r}}\), \(\mu_{0}\) is the permeability of free space at 4\(\pi\)\(\times\) 10\({}^{-7}\) H/m, \(\mu_{\mathrm{r}}\) = relative permeability, and \(\sigma\) is the conductivity in S/m.  
The system being investigated is a commercial off-the-shelf-based system provided by [29] as being certified for repeatable use. The tests undertaken are similar to the tests carried out in [28], where the same sample set is being used to perform robust comparative studies. The output goal of this work was to be able to differentiate different surface qualities based on the detected electromagnetic response waveform. To address the uncertainty of measurements and, specifically, the conditions encountered in service, it was observed that the surface quality of the material has a considerable influence on the extracted sensor response. Therefore, to mitigate against these effects, a design of experiments should demonstrate the differences in sensor responses based on the material surface quality (the same base material will remain constant throughout the tests).