Data usage in the internet of production: development of a process database for data-driven modeling

Abstract In modern production plants, the data generated during production, is essential for optimizing products and processes. Modern machine tools have numerous sensors capturing data at high sampling rates, resulting in vast amounts of data. Storing this data is challenging due to hardware and software limitations, and creating meaningful models to analyze it is a central issue. This challenge is evident in the “Internet of Production (IoP)”, which aims to collect and use data from various sources across the value chain. This paper presents a novel approach for storing and processing raw production data in an aggregated state in a central database. The method enables the identification of data similarities and the derivation of new models from production data, enhancing the understanding of complex relationships. The approach is demonstrated using cutting machine tools as an example.