Lechler T, Sjarov M, Franke J (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 96
Pages Range: 230-235
DOI: 10.1016/j.procir.2021.01.156
The current trend in production optimization extensively relies on data analytics methods such as statistics and machine learning algorithms, aiming at the exploration of undiscovered relationships in existing production systems. When real processes are monitored and analyzed, only the actually implemented features and relationships can be observed. This limits the amount of detectable interdependencies, since prolonged execution of defective processes is often avoided due to negative impact on the running production system. To discover additional insight into production systems, their digital representations i.e. the multi-domain simulation models can be leveraged. Based on these models, Data Farming allows to gain even more insight on production systems. First, major simulation experiments are executed automatically to generate new data sets. Second, the resulting data is analyzed using Data Mining methods to gain additional insight into simulation models and as a result knowledge over the real production system itself. Thus, using Data Farming approaches avoids test scenarios on the production system, which would lead to negative effects on productivity. At the same time, the quality of the simulation model, especially in its border areas, has to be precise enough to make valid statements on newly discovered interdependencies. This paper presents the current state of Data Farming applications in the context of production systems via a review on existing theory, applications and methods. On this basis, the potentials of Data Farming in the context of production systems as well as current challenges in its implementation are pointed out. In conclusion, new use cases for further work are presented.
APA:
Lechler, T., Sjarov, M., & Franke, J. (2021). Data Farming in Production Systems - A Review on Potentials, Challenges and Exemplary Applications. Procedia CIRP, 96, 230-235. https://dx.doi.org/10.1016/j.procir.2021.01.156
MLA:
Lechler, Tobias, Martin Sjarov, and Jörg Franke. "Data Farming in Production Systems - A Review on Potentials, Challenges and Exemplary Applications." Procedia CIRP 96 (2021): 230-235.
BibTeX: Download