Another topic for ML methods is predictive maintenance. To date, maintenance is usually preventive–which means too early–or reactive–which means too late. However, with the right data analysis, companies can predict when a particular part might fail and replace it at the right time thereby reducing the downtime of production.

Besides, completely new business models based on the available data will be feasible. A well-known example is selling a service instead of a machine. The customer rents the machine and pays for the usage. However, this only works if the manufacturer can guarantee a certain service level, for example, 99 per cent availability. Data analysis and ML help suppliers to predict that the machine will work as required.

Latest research activities

The most dominant research topic in ML is deep learning, i.e., deep neural networks. These methods can be used to solve complex problems that were previously thought to be reserved for humans like the game of Go. Reinforcement learning is important for planning actions. Transferred to production, one can imagine a robot that no longer needs to be programmed, but continuously learns and improves from its actions. This is similar to the “trial and error” learning known from children.

Another topic is meta-learning, i.e., learning how to learn. This principle of human learning aims at the most efficient use of data. An example of this is the transfer of what has already been learned to similar tasks.

Last but not the least, the interpretability or comprehensibility of the results in ML is a further field of research. Most ML methods are a black box: One enters data into the algorithm and obtains results, but it is not comprehensible how the result is achieved.

In the near future, further benefits for productions can be expected from the basic and application-oriented research in ML. Politics supports the research as well: For instance, the German Federal Government will invest three billion Euros by 2025 to make the slogan “AI made in Germany” an international trademark. Similar initiatives are going on in other countries.


Learning Robots

In addition to productions, industrial robotics is also a major recipient of ML’s increased research activities. ML is more and more used for the intuitive instruction of robots. Programming times of industrial robots are being shortened by the use of automatic trajectory planners and program generators. AI can also improve robot performance. Accuracy increase, speed optimization, or service life improvement can be achieved by typical methods such as genetic algorithms or reinforcement learning.

Additionally, image processing is a key technology for identifying and locating objects or for capturing environments. AI and ML algorithms are now widely used to optimize recognition performance. The Fraunhofer Institute for Manufacturing Engineering and Automation IPA is further developing the software for bin-picking. While new objects were so far entered manually using CAD data, the software shall teach itself the handling of unknown or complex objects using ML methods. In addition, even noisy or incomplete sensor data should lead to reliable gripping hypotheses. Gripping strategies for parts, for example, with a risk of jamming, should be learned as well.

These are all advances that expand the application area of industrial robots in production and thus contribute to further increase the degree of automation in production.