Manufacturing processes
Intelligence for production
Digital networking and Industry 4.0 can now also be easily introduced for small and medium-sized companies. In view of the current shortage of skilled workers, one key lies in the simple programming of production processes. On the fringes of the Automatica trade fair, editor-in-chief Daniel Schilling spoke to Dominik Bösl, CTO at Micropsi Industries, about artificial intelligence in the production chain.
What is holding back the introduction of artificial intelligence and, more generally, robotic control systems into the production chain, and what differences are there between companies?
The biggest obstacle is the perceived or actual complexity of the task. It is not immediately clear what needs to be done. What does the market offer and what standards apply? Above all, however, the programming is a deterrent in the end. However, we have two customer groups at Micropsi: The inexperienced and newcomers on the one hand are looking for ready-made solutions for specific production requirements in the company, across all industries. Robotics, control systems, tools and software must interlock and function seamlessly. On the other hand, we also have top companies from sectors such as automotive and aerospace that want to go beyond traditional automation. With our Mirai control system, we offer both a suitable solution.
What do you need to look out for if you want to use robots for production tasks?
There are many points. One very important aspect that we have dealt with a lot is how to deal with variance. In other words, how the robot deals with it when it does not find exactly the situation for which it was programmed. A simple example is when the workpiece is slightly displaced. Our AI is now able to deal with this problem well, making it easier to introduce in the factory. This also significantly improves the precision of the work. Precision has long been a problem when using robots in production. However, we have implemented an application at Siemens Energy, for example, in which two-millimetre drill holes are automatically filled with solder paste. It works without any problems.
What about the security and protection of production data?
Our approach is to teach the robot locally and then use the recordings to train the neural network in the cloud. In the end, the control software is only executed locally so that the necessary security is guaranteed.
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