September 22, 2025. Intelligent algorithms could soon provide early warning of painting errors and machine failures. A research team from the Fraunhofer Institute for Manufacturing Engineering and Automation IPA has carried out a series of painting tests on plastic components and trained an artificial intelligence with the resulting data.
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The bumper of a car rests on a frame wrapped in aluminum foil. The arm of a robot approaches from the left, screeching. It assumes its starting position, pauses for a moment and then starts spraying paint. The robot repeatedly pauses briefly and changes position in order to paint the bumper evenly from all sides. However, the special feature of this scene from the Fraunhofer IPA’s paint shop remains largely hidden from the human eye: the painting process is monitored by artificial intelligence (AI).
The only thing that stands out is the inline laser detector from the company AOM, which is attached above the robot’s spray nozzle. It records the number, size and speed of the paint droplets. This information flows into a database. All measurement and process data from 30 different sensors are collected there. This includes data from the b+m system control: speed, voltage, valve circuits, the amount of paint used, the amount of air that directs the paint droplets, and so on. In addition, there are the results of the measurements taken on the finished painted bumper and the visual inspection by an experienced master painter: paint layer thickness (checked by the company Helmut Fischer), color tone, gloss, waviness, dirt inclusions.
Painting is still considered a process that cannot be consistently controlled. There is a risk of rejects, system failures and reworking because, for example, it is very often not possible to maintain the specified paint layer thickness everywhere. Oliver Tiedje, Head of the Coatings and Multifunctional Materials business unit at Fraunhofer IPA, set out to use AI to reduce the number of errors in the painting process and machine downtimes in the “pAInt-Behaviour” research project.
Tiedje and his team therefore carried out a series of painting tests on plastic components from the automotive and commercial vehicle sector in the Fraunhofer IPA’s painting pilot plant. Before each individual test, they changed the settings on the painting system, deliberately accepting errors. The inline laser detector and the other sensors on the painting system recorded everything and filled the database with quality data such as painting errors and coating thickness measurements, as well as with process data from the system control.
A research team led by Brandon Sai, head of the Data-Driven Production Optimization research team at Fraunhofer IPA, had the database evaluated by two different machine learning methods. This resulted in a detailed AI behavior model that is now able to detect impending quality deviations in the painting process at an early stage and identify their causes. This allows the painting process to be continuously optimized without the need for manual intervention.
“The results from our experimental painting processes can be transferred to any other products,” says Tiedje. In recent months, the scientist has submitted a funding application for a follow-up project in which he would like to implement his AI-optimized painting process in practice. He is now looking for cooperation partners from industry.
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Photo: Fraunhofer IPA/Photo: Rainer Bez
