# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
## 5 Discussion  
The processing of data leads to the need to characterize the datasets with respect to the V-concepts. However, the latter, especially veracity and value, are not defined in a straightforward manner. Processes themselves, however, appear to have different geometrical configurations, different defects, and different monitoring approaches, leading to differentiated datasets and diversified data processing and machine learning techniques. This is quite a hindrance to the definition of the V-concepts in a unified matter. The different nature of the signals (i.e., photos vs. videos), as well as the various steps of machine learning, namely denoising, feature extraction, and classification (or regression), seem to provide a more systemized way of matching the 7 Vs with monitoring characteristics. For both the cases of RSW and laser welding, it can be noted that the veracity and the value are quite case-dependent and are highly coupled to both the performance and the efficiency of the quality monitoring systems. Figure 10: Second case study—Geometric mean of TPR and TNR. Figure 9: Gradient during training in the second case study—P2-F1 case. It has to be noted that, deliberately, all the aforementioned metrics only refer to the second stage of machine learning, the classification itself. In general, the first stage is about extracting the features that have been taken for granted herein. This is particularly valid in the case of deep learning, where big data would indeed be relevant. Herein, in the second case, the PCA algorithm has been utilized, as it has been proven to work, while in the first one, the extraction of variance is used. This reduces the \"computational load\", as for each dataset training sample, the amount of information of 32 \(\times\) 32 pixels \(\times\) 5,120,000 frames is reduced to 3 features and 10 features for cases 1 and 2, respectively. It is, however, noted that Figure 1 could be extended to incorporate feature extraction as a fourth characteristic of the classification problem. In that case, the technical value of the dataset could be defined as Equation (1) below. This does not include, of course, the business part of the value. \[\text{Value}=\frac{\text{true information of the dataset towards successful training}}{\text{the size of the dataset}}, \tag{1}\]  
As far as veracity is concerned, partial metrics per aspect (i.e., usability and trustworthiness) have to be taken into consideration. Taking into account usability in particular, a tentative definition for veracity can be the following (Equation (2)), given that a case refers to a specific change in the characteristics. This is closely related to the concept of robustness [54; 55]. The \"scenario\" refers to the potential transformations applied to the dataset. This could also cover the case of security [56]; however, this exceeds the purposes of the current work. \[\text{Veracity}=\frac{\text{the number of the scenarios where the performance is good}}{\text{the number of all the scenarios}}, \tag{2}\]  
This could transform a bit the way that the 7 Vs are regarded. As a matter of fact, they can be considered filters that gradually transform the required size of data D. Thus, a roadmap could be formed (Figure 11). The various Vs are shown here as filters, transforming the need for data (green circular disks). They help a company, in a way, respond to critical questions (red rhombuses). Figure 11: A roadmap for integrating Vs in metamodeling. Red nodes are critical questions; green nodes are metrics. The relationship between the various data sizes can be algebraically given through coefficients \(\eta\). The business value, however, is also up to other factors, denoted by the letter \(B\), implying the need to change the overall workflow within the company. Using big data analytics in manufacturing carries significant ethical, social, and environmental implications.