# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
###### Abstract  
Quality monitoring of manufacturing processes is a field where data analytics can thrive. The attributes of the data, denoted with the famous 7\(\mathrm{V}^{\prime}\), can be used to potentially measure different aspects of the fact that data analytics may be referred to, in some cases, as big data. The current work is a step towards such a perspective, despite the fact that the method, the application and the data are coupled in some way. As a matter of fact, herein, a framework is presented through which a heuristic match between the big data attributes and the quality monitoring characteristics in the case of manufacturing is used to extract some insights about the value and the veracity of datasets, in particular. The case of simple machine learning is used and the results are very interesting, indicating the difficulty of extracting attribute characterization metrics in an a priori manner. Eventually, a roadmap is created with respect to integrating the data attributes into design procedures. quality monitoring; big data; data attributes; machine learning +
Footnote †: journal: _sciences_  
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The work focuses on the other Vs, as they are given in the table hereafter (Table 1). In general, these characteristics interact with the computing system, so extra characteristics could be involved [13]. The 7 Vs are interrelated most of the time, depending on the application. For instance, in real-time imaging processing applications, a high-speed camera will typically result in a large volume of data [19]. Thus, velocity is, in this case, directly linked to the volume of the data. In multimodal sensing applications, an increase in data volume is directly related to the number of sensors used for monitoring a particular process or asset [20]. Regarding the size of the data in bytes, estimations lie on the spectrum of peta-exa-and zeta-bytes [21; 22], while at the same time, there is a close link with cloud systems [23; 24; 25] as well as with distributed computing systems [26]. In addition, it is stated that there are a variety of methods applied in the area of big data [27; 28]. It is worth noting that there is the case of randomized numerical linear algebra that suits perfectly in this case [29]. Herein, however, the case of classification through machine learning is considered in order to extract some conclusions. In particular, as manufacturing is quite relevant in the case of big data, it seems that there can be some insight. Additive manufacturing [30], supply chain management [31; 32], cyber-physical systems [33], fault diagnosis and early warning [34], manufacturing processes [35] as well and product development [36], are all thematics that can be potentially related to the 7 Vs. More particularly, in the current work, the focus is on welding [37; 38], the quality monitoring of which is expected to easily increase the levels of either one of the 7 Vs. As aforementioned, volume and velocity have been associated with computational complexity [11]. This can be the beginning of the 7V's quantification. However, this is not enough; a space characterized by V-related axes could be used for the full definition. Thus, herein, the concepts of veracity and value are investigated within the context of specific applications. At the same time, the challenge remains to quantify these characteristics using a simple, intuitive, and a priori method. The approach described in the following section aims at demystifying this procedure for the case of veracity and value, in particular, through specific cases of welding quality monitoring and thus classification. It is noted that for the case of veracity, only the part of applicability is checked here.