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Increasing the efficiency of painting processes with artificial intelligence

Increasing the efficiency of painting processes with artificial intelligence

Together with a research team from Fraunhofer IPA, Helmut Fischer wants to make the painting of plastic parts in automotive and commercial vehicle manufacturing more efficient with the help of artificial intelligence (AI).

The painting process and its quality are very difficult to capture due to complex interactions between process, paint material and environmental conditions, which is why painting is still considered a process that cannot be consistently controlled. There is a risk of rejects, equipment failures and costly rework, since, for example, the specified coating layer thickness often cannot be maintained everywhere.

This is where a research team from the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, together with Helmut Fischer and other project partners, comes in:

The aim of the research project is to develop models that for the first time make it possible to predict the quality achieved from the process data of the paint lines. With meaningful data of high quality in the future, the reliability of the painting processes will increase massively and the flexibility of the painting lines can be raised to the necessary level for large-scale mass-produced products - with the lowest possible reject rates.

The project results will enable significantly more efficient painting processes. A 30 percent reduction in defects, a 10 percent reduction in annual paint consumption and a 10 percent reduction in the start-up time for new paints, as well as a 20 percent reduction in downtimes thanks to diagnostic behavior model analyses can be expected here.

The research team wants to achieve this by first combining the quality data, for example visible coating defects and, in particular, the measured data on coating film thickness, with the process data from the system control. The data will then be used to create a fine-granular behavior model, which will be evaluated using a machine learning method. The algorithms should detect impending quality deviations at an early stage and also immediately point out their cause.


Use of AI in coating thickness measurement using terahertz radiation

Helmut Fischer's research project focuses on strategies for using machine learning for material diversity in paint layer thickness measurement using terahertz radiation. An essential factor for market acceptance and market penetration for terahertz-based coating thickness measurement is a presentable economic efficiency of the necessary investments at the end user. This seems possible with AI networking of quality data in the paint shop, especially in the combination of coating thickness measurement, paint spray mist measurement as well as visual inspection. Further planned work on automated parameterization and calibration using machine learning and artificial intelligence may also lead to potential cost savings in other metrology product lines of Fischer - especially the analysis of X-ray fluorescence spectra - through higher efficiency in other industries.

The research project "Increasing the efficiency of painting processes through multilayer networking of process and quality data using self-learning behavior models" (pAInt-Behaviour) will run until May 31, 2024 and is supported by the German Federal Ministry of Education and Research with almost 1.3 million euros.

Schematic of the learning multi-layer model, in which data is summarized within each layer using machine learning, and the relationships to quality variables are mapped by AI
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