Topic: “A Review of Data Science Approaches to Quality Control in Manufacturing”
Data in manufacturing are quite different to data in other domains, e.g., business or social. Data Science approaches directly depend on the data, hence different data asks for different approaches. This overview provides a list of important problems and challenges for data science approaches to quality control in manufacturing.
We furthermore associate the identified problems and challengesto individual layers and components of a functional setup, as it can befound in manufacturing environments today. Additionally, we extend and revise this functional setup and propose a software architecture asa visionary blueprint.
Bernhard Mitschang is professor for Database and Information Systems and head of the department ’Applications of Parallel and Distributed Systems’ that is part of the Institute of Parallel and Distributed Systems at the Universität Stuttgart, Germany. Both research and teaching spectra of his department cover on one hand data-intensive applications ranging from business applications to engineering systems and on the other hand fundamental data management techniques, data analytics as well as scalable data processing architectures.
Since 2013, he is CEO of the Graduate School of Excellence on advanced Manufacturing Engineering and head of the Technology Partnership Lab at the university.