Aqueous parts cleaning is essential for technical cleanliness in the industry, yet it is highly energy- and resource-intensive. Facilities often operate on the "more is better" principle, as the initial level of contamination on the components is usually unknown and precise bath monitoring is lacking. Small and medium-sized enterprises (SMEs), in particular, often lack the necessary expertise, leading to inefficient processes.
This is where CleanGreen comes in: the goal is to develop AI-driven data analysis for needs-based, sustainable process control. Data collection combines the system control, sensors for bath monitoring, and a laser scanner that quantitatively and qualitatively detects contamination prior to the cleaning process. This data not only optimizes the cleaning itself but also improves subsequent processes such as painting or electroplating, while serving quality control purposes.
The innovation is being integrated into a test system at the Fraunhofer IST and enhanced with optical measurement technology. This will be followed by a prototype implementation at an industrial partner's site. Through this intelligent control system, process times can be shortened, and the consumption of energy, water, and chemicals can be significantly reduced in both new and existing facilities.
Project Duration: 07/2026 - 06/2029
Funded by: Federal Ministry for Economic Affairs and Energy (BMWE)