Industry 4.0
Robots and machines position workpieces precisely for the next processing step, join components together, or even carry out specific processing steps. Currently, the way they move is controlled by internal position measurements. The NCCR Automation is going a step further and developing control strategies that incorporate measured values from outside the machine – from either the production process, or the end product. For example, sensors can monitor how well a robot sprays a component layer by layer, or where the quality of the end product is defective. We want to exploit this information to manage the production process better, so as to increase productivity and improve quality. To do so, we are developing new, self-learning control algorithms.
By integrating control loops that access data from the production process, we are looking to optimise additive manufacturing processes such as selective laser melting (SLM). In addition, the automated process steps should be able to learn from experience and adapt more rapidly to new parameters, for example when the material or component shape changes in the next batch. These advanced control mechanisms we will be put to practice in our lab facilities and through collaborations with industrial partners.
