## 2 Measuring System, Materials and Methods  
### Methodology  
#### 2.1.1 Data Acquisition and Exploration Process  
Knowledge acquisition from the measurements utilizing non-destructive testing (NDT) methods can be modeled using a framework analogous to the classic CRISP-DM (Cross Industry Standard Process for Data Mining) [67; 68; 69]. The CRISP-DM model is an iterative algorithm with seven stages and feedback loops.  
The initial step entails a comprehensive understanding of the problem and the physical phenomena associated with the employed NDT method. This phase encompasses a thorough literature review and simulation studies.  
The next step is the preparation of samples and measurement system setups.  
The subsequent step focuses on data comprehension. During this phase, measurements are carried out. Attributes used to represent waveform features are defined and used to extract association rules between sample changes and corresponding changes in the acquired measurement. The rules are extracted through visualization techniques, statistical analyses, and dedicated algorithms such as Apriori [70]. Additionally, at this stage, measurement results are compared with simulation outcomes. Preliminary analyses often reveal the necessity for adjustments to the laboratory setup and may require repeated measurements, frequently necessitating multiple iterations.  
Data preparation is undertaken to facilitate identification using machine learning in the third step. This process may involve outlier removal, normalization, quantization, discretization, curve smoothing, etc. Furthermore, this stage may necessitate revisiting prior steps. For instance, while outlier removal might uncover new previously obscured relationships, curve smoothing can either enhance or obscure these relationships (thereby, it is essential to choose appropriate tools and their configurations).  
The modeling stage involves developing the identification model. At this step, attributes are modified and selected. Techniques such as aggregation, filtering, wrappers, or more advanced methodologies like the rough set theory [71; 72] or Principal Component Analysis (PCA) are applied for these purposes. Next, various classifiers and identification algorithms are evaluated using cross-validation or an expert system.  
At the next stage, the researcher possesses a fully operational identification model. This step aims to resolve any outstanding uncertainties and validate the appropriateness of specific solutions and configurations. A negative assessment of the model necessitates a comprehensive reevaluation of all preceding stages, commencing with understanding the method and the characteristics of the object under investigation (as various influencing phenomena may need to be adequately considered).  
The final step is model deployment. In this stage, new samples with unknown parameters are identified. The efficacy of this identification process is well established and known as it has been subjected to prior examination.  
Figure 4 illustrates the sequential stages of knowledge acquisition and the most frequently encountered feedback loops inherent to this model, which has been adapted to the specifics of NDT.