TY - GEN
T1 - Learning Systems for Manufacturing Management Support
AU - Gursch, Heimo
AU - Wuttei , Andreas
AU - Gangloff, Sophie
PY - 2016
Y1 - 2016
N2 - Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop floor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning fields and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the field of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance. Copyright © 2016 for this paper by its authors.
AB - Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop floor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning fields and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the field of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance. Copyright © 2016 for this paper by its authors.
M3 - Beitrag in einem Konferenzband
T3 - CEUR Workshop Proceedings
BT - 1st International Workshop on Science, Application and Methods in Industry 4.0
T2 - 1st International Workshop on Science, Application and Methods in Industry 4.0 - co-located with the International Conference on Knowledge Technologies and Data-driven Business
Y2 - 19 October 2016
ER -