Fast, efficient, reliable: Artificial intelligence in BMW Group Production

Munich, Germany. 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.”

 

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.

Christian Patron: “We rely entirely on the experience and expertise
of our employees in these efforts. They can judge best at which
production steps an AI application may improve quality and efficiency.
We deliberately keep the setup and implementation of such applications
simple. Their operation requires no advanced IT proficiency.”

 

AI eliminates pseudo-defects

At the press shop, flat sheet metal parts are turned into
high-precision components for the car body. Dust particles or oil
residues that remain on the components after forming can easily be
confused with very fine cracks, which occur in rare cases during the
process. Previous camera-based quality control systems at the BMW
Group’s plant in Dingolfing, Germany, occasionally also marked these
pseudo-defects: deviations from the target, even though there was no
actual fault. With the new AI application, these pseudo-defects no
longer occur because the neural network can access around 100 real
images per feature – i.e. around 100 images of the perfect component,
100 images with dust particles, another 100 images with oil droplets
on the component, etc. This is particularly relevant in the case of
the visually close calls that have previously led to pseudo-defects.

The BMW Group’s Steyr plant and the BMW Group Data Analytics Team are
also successfully working on eliminating pseudo-defects. Presumed
irregularities in torque measurement in the engine cold test later
often turn out to be insignificant. Before introducing the AI
solution, however, such results led to complex manual inspections and
further test runs, up to and including hot tests with fuel. The
analysis software was trained based on many recorded test runs and
thus learned to distinguish between actual and presumed errors.

 

AI ‘in control’: Integrating artificial intelligence with
facility and robot control systems

The first smart AI control application at the BMW Group celebrated
its premiere at the BMW Group’s Steyr plant. This application speeds
up logistics processes by preventing unnecessary transports of empties
on conveyor belts. To this end, the containers pass through a camera
station. Using stored image data marked by employees, the AI
application recognizes whether the container needs to be lashed onto a
pallet or whether – in the case of large, stable boxes – no additional
securing is required. If no lashing is required, the AI application
directs a container by the shortest route to the removal station for
the forklift truck. Containers that must be additionally secured, on
the other hand, are guided directly to the conveyor section with the
lashing system and only then to the removal station located behind.
Previously, all containers had to be transported to the removal
station for large containers. From there, the containers that required
additional securing had to be forwarded – and would only reach the
lashing facility and finally the correct removal station after taking
this detour.

 

Besides the application in Steyr, AI is behind numerous other logistics
innovations
at the BMW Group. It also supports virtual layout
planning, which creates high-resolution 3D scans of buildings and
factories. Artificial intelligence ultimately contributes to the
recognition of individual objects in the 3D scan, such as containers,
building structures or machines. This allows engineers to remove
individual objects from the 3D scan in the 3D planning software and to
modify these individually, which makes it easier to simulate and
understand adaptations on the shop floor.

 

There is already a distinct trend toward using AI applications at the
BMW Group’s plants. The increasing integration of smart
data analytics
, state-of-the-art measurement technology and AI
opens up new opportunities in production management. At the body shop,
for instance, images from the final inspection may show that weld
metal has sprayed out at the same welding point in several car bodies.
Using AI, the control loop can thus be closed and system control or
maintenance cycles be adjusted even faster and more efficiently. At
the paint shops, AI and analytics applications offer the potential to
detect sources of error at such an early stage that errors can hardly
occur any more: If no dust attaches to the car body before painting in
the first place, none has to be polished off later.