The future of automation

The world of manufacturing and industries has never been as dynamic as it is today. Ever since Platform Industrie 4.0 was launched at the 2013 Hannover Messe, we have seen a surge in interest and call-outs to disrupt manufacturing business models like never before.

Artificial intelligence (AI) is set to play a key role in reducing the programming and engineering effort required to create automation solutions. It’s also making control logic more agile and production processes more flexible and precise – especially in industry.

For example, machine-learning algorithms help systems that perform visual quality checks in production plants or image-guided robot systems react much more flexibly to unexpected situations and to quality defects because they can respond automatically during runtime. As a result, they operate much more efficiently, because expert knowledge – for example, regarding the color, consistency, or quality of a product or process – can be transmitted to automation.

      AI makes it possible to perform automation tasks that push traditional solutions to their limits. It’s gradually establishing itself on every level of the Totally Integrated Automation portfolio – from the field to the cloud.

     An AI solution can be adapted to its environment and target application as needed, either at the machine or for solutions across all machines or even plants.In this context, we also have to focus on machine learning (ML), which provides the means to achieve artificial intelligence. AI without machine learning is tightly bound to programming without advanced languages; i.e., machine learning makes it easier to achieve AI compared to conventional means. It conveys the ability of a system (or a machine) to learn without being programmed explicitly. ML deals with training algorithms to learn about specific contexts using massive troves of data. ML can be further achieved through deep learning (DL), decision tree learning, inductive logic programming, etc., with each approach designed and suitable for specific situations.

The field of AI, despite being highly contentious, is well-developed technologically and now beginning to be exploited for various landscapes, most notably in the consumer space. Our aim, however, is to identify how AI can help manufacturing and, in particular, automation.