When I’m asked what the factory of the future will look like, I always say: smart, lean, and green. These are all characteristics that already describe the factories in our global production network at Vitesco Technologies. But when it comes to artificial intelligence (AI) in manufacturing, I think we’re still at the beginning of an exciting journey. AI systems will completely change the way we run our production and enable greater efficiency by improving processes, providing real-time insights, and even predicting the future.
That’s why we have set up competence centres in Germany, France, and China where we develop such innovative AI solutions and implement them in our production lines - or as we like to call it: We are giving our production systems a brain!
We see the greatest opportunities for us to profitably use AI in the areas of process monitoring and quality control. Here we are focusing on three key projects:
- Predictive Quality
- Prescriptive Quality
- Predictive Maintenance
Prescriptive quality is about deriving actions based on the data collected to ensure that a future desired outcome does or does not occur. The question is, how do I have to adjust my process parameters based on historical data so that I do not produce defective parts. This is also called closed-loop process control.
The first step is to automate and objectify quality decisions to eliminate subjective influences caused by these manual inspections. Because the more operators are performing manual inspection and the greater the diversity of parts to be inspected, the higher the potential error rate. With AI, we can reduce human judgement bias by 300 percent.
Subsequently, further process data must be generated from individual machines or lines to better understand the processes and their influences. Analysing these enormous amounts of data and recognising patterns using neural networks enables optimised control of processes and can reduce scrap before it is produced.
We have already started to replace manual inspection with automated visual inspection using AI. In the process, algorithms have been developed and trained based on a large number of images, which can then independently distinguish between good and bad parts.