Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case

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Abstract
Quality monitoring of manufacturing processes is a field where data analytics can thrive. The attributes of the data, denoted with the famous ‘7V’, 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.

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