In modern, automated production facilities, large amounts of data are generated in addition to the actual products. Operating data of machines and industrial robots or measurement results of sensors are sources of such data.
The evaluation of this data treasure trove holds enormous optimization potential: processes can be accelerated, quality can be improved, machines can be maintained preventatively and robots can be employed more efficiently.
Such applications often rely on Machine Learning methods for processing large amounts of data. Artificial neural networks in particular are able to learn from a large sets of sample data and to develop models. These models then help production engineers, for example, to optimize manufacturing. However, such applications require very powerful computers.
Especially for applications in industry 4.0 and logistics, it is not always possible – for example due to a lack of bandwidth for data transmission – to send all information to a powerful central computer. Instead, the devices that record the data, such as sensors, have to take over part of the processing and analysis.
The developers at the ML2R Competence Center are working on making Machine Learning also available on devices with limited computing power and memory. In addition to sensors and hardware that can be linked within the Internet of Things, such devices are smartphones or mobile computers. One research approach is to simplify the ML algorithms so that they require less memory and computing capacity. Another course of action focuses on the development of hardware and software that is optimized for specific learning tasks.