Munich. BMW Group Plant Munich is making increasing
use of applications with artificial intelligence (AI). AI is fast,
reliable and easy to integrate into the various production processes
and, coupled with smart data analytics and cutting-edge measurement
technologies, it opens up new opportunities for more efficient vehicle production.
Robert Engelhorn, Director of BMW Group Plant Munich, is working to
advance the application of these technologies: “At Plant Munich, it
takes about 30 hours to manufacture a vehicle. During that time, each
car we make generates massive amounts of data. With the help of
artificial intelligence and smart data analytics, we can use this data
to manage and analyse our production intelligently. AI is helping us
to streamline our manufacturing even further and ensure premium
quality for every customer. It also saves our employees from having to
do monotonous, repetitive tasks.” As with any innovation, the key
factor is effectiveness: “Our team in production are highly
experienced specialists, so they are the best judges of whether an AI
application can boost quality and efficiency at any given stage of
production,” says Robert Engelhorn.
The options for using AI and smart data analytics are currently being
tested in various areas of BMW Group Plant Munich. In some areas, the
technologies are already in use in series production, such as the
press shop and function validation.
Smart Data and AI in the press shop
The press shop at the BMW Group’s home plant in Munich turns more
than 30,000 blanks a day into vehicle body parts. Since 2019 each
blank has been given a laser code at the start of production so the
body part can be clearly identified throughout. This code is picked up
by the iQ Press system, which records material and process parameters
– such as the thickness of the metal and oil layer, and the
temperature and speed of the presses. The parameters are then related
to the quality of the parts produced.
Uploaded to the cloud in real time, the data is immediately available
in its entirety for the production team to gain a clearer picture of
the manufacturing process. iQ Press data is an important tool for
them, as it eliminates the need for each body part to be checked in
minute detail, in quality control for example, and picks out only
irregularities that require action.
Artificial intelligence also offers potential to identify recurring
patterns in a process, based on the data collected, to support
continuous optimisation. So, as well as improving the efficiency of
production systems, iQ Press helps to further increase hourly output
from the press shop.
Predictive maintenance in the body shop
Body shop robots are fitted with a combined total of over 600 welding
tongs. If the tongs ever need replacing unexpectedly, it costs
significant time and money. Moreover, many of the robots are difficult
to access, so dismantling and replacing their tongs can take hours.
Until now, the condition of tongs has been monitored by eye, by a
member of the production team. But in recent months, the maintenance
specialists at Plant Munich have been fitting sensors to all the tongs
to measure friction levels three times per shift and report any
abnormalities. The data they produce is constantly evaluated by
software, allowing potential machine failures to be predicted. Martin
Hilt, Innovation and Digitalisation Officer at Plant Munich, explains:
“Because we have the sensors and collect their data in a cloud, we can
now monitor round-the-clock whether any maintenance work is needed.
So, we can plan any replacements better and potentially schedule them
for a production break.”
Dust particle analysis in the paint shop
Despite comprehensive cleansing systems, vehicle bodies can pick up
dust particles as they make their way to the paint line. Though
invisible to the human eye, the particles can affect the quality of
the finish. Until now, potential defects have gone undiscovered after
the painting process, revealed only by the automatic surface
inspection. They then had to be reworked, or the bodies repainted completely.
Now, however, every paint shop system incorporates sensors that
measure dust levels and allow the quality of paintwork to be
predicted. “We can now tell quickly if the environmental parameters
are not quite right at some point, either within the paint shop or in
one of the buffer areas. It takes a lot of data to do this, which we
collect throughout the process, evaluate historically and analyse in
real time,” explains Martin Hilt.
Over the last few months, a further special sensor developed by Plant
Munich has been measuring dust levels on body parts at the beginning
of the painting process, before and after the emu feather rollers. In
the future, when dust levels are too high, car bodies will pass
through the paint shop untreated and be sent for further cleansing.
AI-based image recognition in assembly
AI projects in assembly mainly focus on automated image recognition.
Here, the technology is used to evaluate images of a component and
compare them in milliseconds with hundreds of other images from the
same sequence. The system then identifies any deviations from the
norm, such as parts that are incorrectly positioned or fitted, or absent.
At Plant Munich, automated image recognition allows the production
team to identify whether the hazard warning triangle, wiper caps and
door sills have all been correctly fitted to each car. Previously,
small bubbles in the foil cover of a door sill were often enough to
prevent the conventional camera gates from seeing if the logo on the
door sill was correct. But now an associate photographs each part
concerned in turn and can even use the mobile equipment to check parts
that are more difficult to access. Distance, angle and light hardly
have any effect on AI evaluations, which reveal within fractions of a
second whether everything is in place or not.
The AI system is trained by associates. They start by photographing
the component concerned from various perspectives and marking
potential deviations on the images. This allows them to develop an
image database that can be used to build up a neural network for
evaluating the images. Evaluations are carried out fully
automatically, and the machine decides by itself whether or not a part
meets all the specifications.
RFID identifies components in the vehicle
Radio Frequency Identification (RFID) allows components to be
identified automatically and contactlessly throughout the value chain.
“Our goal is to save production workers from having to scan components
manually, and simultaneously to streamline manufacturing even further
by ensuring the right components are fitted to the right vehicles,”
says Martin Hilt. RFID is currently being used in seat production at
Plant Munich but will soon be used throughout the vehicle assembly as well.
Smart RFID labels required for the system are applied before the
component leaves the supplier. They remain in place throughout
production, allowing line-side antennae to pick up every labelled
component within each car as it passes.
Function validation with the Comfort Access robot
Comfort Access was first introduced in the current BMW 3 Series. A
small team from Electrics/Electronics Validation in Munich has now
developed a special robot to validate its integration.
Vehicles with Comfort Access use three exterior antennae to generate
a three-dimensional electromagnetic field around the car. When the
driver enters the field, the system recognises the car key. At about 3
metres from the car, it switches the Welcome Light on to illuminate
the area outside the driver’s door. At about 1.5 m, the doors unlock –
and relock automatically if the driver walks away.
Until now, this special feature has been validated manually, with
parameterisation in development alone taking two days per vehicle. The
Comfort Access zones and the influence of production processes on them
are then checked manually again in the plant, before production
begins, taking into account the various country-specific requirements
and equipment features, such as trailer couplings. All in all, it is a
lengthy process and not always entirely accurate, given the multitude
of different functions.
To solve the problem, the BMW Group and the University of Applied
Sciences (HTW) in Dresden have developed a measurement robot that
autonomously circles the vehicle several times in a pre-defined
pattern to determine the strength of the magnetic field at various
required points. Attached to the robot is a box containing the car
key. The box can be set at different heights to reflect the different
ways a driver might carry it: in their hand, their sports bag or a
breast pocket, perhaps. As soon as the robot detects the vehicle
electronics locking or unlocking the doors, its inbuilt Lidar scanner
measures the distance between the key and the vehicle, and surveys the
vehicle’s surroundings. The data that is generates goes straight to a
central computer, where it is portrayed as a graphic.
The advantages of the system are obvious: “This robot is not only
much faster, it’s also more precise. The results we obtain are highly
detailed and, most importantly, objective. So we can even start
validating the function before the car has its first test-drive,”
explains Martin Hilt.
Vehicle location in the production system
The specialists responsible for product integration at Plant Munich
ensure stable processes to deliver defect-free vehicles throughout.
They are also responsible for integrating pre-series vehicles into
production to allow a smooth official production launch with series
quality right from the start.
“Since the start of this year, our specialists have been using a new
app that notifies them as soon as the pre-series vehicle they are
tracking reaches a specified point in assembly. It allows them to
locate any car they want – so they can check, say, a particular
combination of equipment features,” says Martin Hilt.
The new app not only replaces the manual process but also improves
validation. In the future, it can also be used on series vehicles.