Official release: Artificial intelligence (AI) is on the rise in automotive production. Since 2018, the BMW Group has been using various AI applications in series production. One focus is automated image recognition: In these processes, artificial intelligence evaluates component images in ongoing production and compares them in milliseconds to hundreds of other images of the same sequence. This way, the AI application determines deviations from the standard in real time and checks, for instance, whether all required parts have been mounted and whether they are mounted in the right place.

The innovative technology is fast, reliable and, most importantly, easy to use. Christian Patron, Head of Innovation, Digitalization and Data Analytics at BMW Group Production: “Artificial intelligence offers great potential. It helps us maintain our high quality standards and at the same time relieves our people of repetitive tasks.”

Artificial intelligence

At the BMW Group, flexible, cost-effective, AI-based applications are gradually replacing permanently installed camera portals. The implementation is rather simple. A mobile standard camera is all that is needed to take the relevant pictures in production. The AI solution can be set up quickly too: Employees take pictures of the component from different angles and mark potential deviations on the images. This way, they create an image database in order to build a so-called neural network, which can later evaluate the images without human intervention. Employees do not have to write code; the algorithm does that virtually on its own. At the training stage, which may mean overnight, a high-performance server calculates the neural network from around 100 images, and the network immediately starts optimizing. After a test run and possibly some adjustments, the reliability reaches 100%. The learning process is completed and the neural network can now determine on its own whether or not a component meets the specifications.

Even moving objects are reliably identified largely independent of factors such as lighting in the production area or the exact camera position. This opens up a wide range of potential applications along the entire automotive process chain, including logistics. In many cases, the AI technology relieves employees of repetitive, monotonous tasks such as checking whether the warning triangle is in the right place in the trunk or whether the windscreen wiper cap has been put on.

Artificial intelligence can also perform more demanding inspection tasks

In the final inspection area at the BMW Group’s Dingolfing plant, an AI application compares the vehicle order data with a live image of the model designation of the newly produced car. Model designations and other identification plates such as “xDrive” for four-wheel drive vehicles as well as all generally approved combinations are stored in the image database. If the live image and order data don’t correspond, for example if a designation is missing, the final inspection team receives a notification.