Dürr Showcases Modular Process Configuration at Virtual Open House

Dürr recently held a virtual open house to showcase new products designed “to increase efficiency and profitability in manufacturing and reduce resources for large volume manufacturers and small electric car startups alike.”

This is the company’s 12^th such innovation showcase, but this time the event was held virtually. During the course of four days, different time zones and languages the open house offered a mix of presentations, interviews, and technical demonstrations.

According to Dürr, the focus was on solutions that have the potential to “replace the previous line concept with a future-oriented configuration based around flexible, intelligently connected process modules that can be scaled to meet requirements.”

“What manufacturers need most these days from the painting process is flexibility, cost efficiency, and sustainability. We answer this need with our concept of the paint shop of the future, which overcomes the limitations of the inflexible production line. Instead, the 120 or so individual operations are executed in scalable modules or small sections, in parallel and timed precisely to the needs of the individual body,” explains Jochen Weyrauch, CEO of Dürr. “This makes it easy to extend capacities and channel different vehicle types through the painting process. Ideally, even individual items can be handled while production is underway.”

During the virtual event, Dürr showcased this variable configuration concept from pretreatment to seam sealing to paint application based on its new products.

Intelligent flexibility 

Dürr’s aim is to add flexibility to the entire process chain. Individual stations are custom equipped using the automated guided vehicle EcoProFleet, an automated guided vehicle (AGV) developed specifically for paint shops. In combination with a high-bay storage, the AGV lays the foundation for intelligent material flow control along the whole process. The DXQ control software controls each AGV with a high degree of intelligence in order to ensure modules are always efficiently utilized, right from the pretreatment stage. 

The new modular system design of EcoProWet PT is based on standardized chambers instead of immersion tanks arranged one after another. This is not only more flexible, but also saves a lot of space. Dürr has further automated the subsequent seam sealing stage with the new EcoSealJet Pro applicator and the DXQ software powered by artificial intelligence (AI). The applicator largely does away with the need for manual application, even in hard-to-reach places, saving time and material, and ensuring precision and consistent quality. 

For the actual painting process, the box configuration combines the application of the primer and base coat layers — the interior application and the two exterior applications — into one spray booth, eliminating two of the three usual conveying operations. In addition, it can be used as a standard spray booth with the same robot equipment for both base coat and clear coat, and can handle everything from small cars to pick-ups. 

The overspray-free EcoPaintJet Pro painting system can apply two-component paints (2C) automatically, in addition to two-tone lines for car roofs, color changes, vertical surfaces, and decorative paint applications. The latest atomizer generation, EcoBell4, is said to deliver shorter color change times and high quality automated atomizer cleaning. Low paint and solvent consumption helps to make production more sustainable by reducing VOC emissions. Integrated RFID technology is designed to maximize the lifetime of the components and increase the first run rate.

Artificial intelligence 

Dürr states that the software products from its DXQ family ensure the individual processes interact smoothly in the modular concept, transforming the box-based painting system into a smart factory. A key tool in this is DXQplant.analytics, which uses AI algorithms and machine learning modules to collect and analyze sensor data, messages, and information. This helps the quality managers identify correlations early on, so that errors can be avoided and the first run rate increased. A feedback feature constantly provides the algorithm with information, so that predictions about the need for maintenance (predictive maintenance) or quality status (quality prediction) can become more and more precise.