Aircraft engines must reliably deliver high levels of thrust even under adverse conditions, making precision paramount in the manufacture of modern aircraft turbines. In turbofan engines, the fan that draws air into the turbine has multiple blades with complex, curved geometries. These metal blades are manufactured using a precision milling process that requires a careful machining strategy. Precision milling is only possible if the relative movement of the tool and the workpiece is tightly controlled. Otherwise, the cutting tool can begin to vibrate, causing the blade surface to strike the milling head uncontrollably and damage the tool. Even a slight deviation from specifications rendering a component virtually unusable can be a very costly mishap. "This type of production error can be very costly for manufacturers of high-precision components for aircraft engines," explains Wolfgang Maaß, professor of business informatics at Saarland University and head of the Smart Service Engineering research area at the German Research Center for Artificial Intelligence (DFKI).
But the situation is similar for many other companies, large and small, that manufacture metal components, including those produced by laser cutting. The extreme heat generated during the cutting process can cause the metal to expand in places where it shouldn't. Or, in conventional cutting operations, offcuts can become trapped in the machine, causing it to stop. The result is increased rejection rates and long periods of machine downtime, which cost the manufacturing company material, time, and money. The metalworking industry is a major economic sector in Germany and the EU, and safety and quality standards are stringent. Production processes in the metalworking sector sometimes have rejection rates of around 1%, which doesn't seem like much, but collectively it can have a significant impact on competitiveness,” explains Wolfgang Maaß.
It is possible to reduce the rejection rate through digital simulations that utilize artificial intelligence. By creating a digital twin of the workpiece, everything that happens to a real component can be simulated in a virtual environment, from planning and production to quality assurance. In theory, every aspect of the production chain can be precisely optimized, from the perfect spindle speed for milling to the ideal power density delivered by the laser. But there's a problem. The enormous volume of data required to produce these high-resolution simulations simply cannot be handled by conventional computer systems. Quantum computers, which could provide the necessary computing power, are not yet available. The result: "Currently, simulations are hardly used in practical applications. This is partly because sufficient computing power is not available and partly because specialized data and information are needed, which in turn requires detailed expertise in computational simulations," explains Hannah Stein of the Maaß research team. Currently, metallurgical companies have to make do with lower resolution digital twins and rely heavily on the practical knowledge and experience of their production engineers.
And there is still a long way to go before quantum computers provide the breakneck speed needed to handle massive volumes of data. However, the researchers on the Quasim project are firmly grounded in reality. The consortium of industrial and academic partners is working on short- and long-term solutions that harness the power of quantum systems to deliver enhanced simulations for use in manufacturing scenarios. According to Wolfgang Maaß, the project coordinator, "Our initial studies have shown that by exploiting the principles underlying quantum mechanical systems and using quantum-based machine learning strategies, we can solve algorithmic problems much faster." Although current quantum computers are still in their infancy, the underlying technology can already be applied in areas where conventional computers would be stretched to their limits, requiring enormous amounts of time to complete a calculation.
Researchers are using various quantum computing methods to explore ways to make complex simulations faster and more suitable for practical applications. The work involves applying quantum computing technologies to conventional simulation methods based on mathematical models from physics and materials science. The team is also investigating quantum-based machine learning methods. By comparing these new approaches with conventional methodologies and evaluating the effectiveness of different solutions, the team is developing innovative solutions that could find practical application in the near future. The results are already being integrated into existing simulation methods. “We are currently developing the first prototypes. So far, the most promising results have been obtained using hybrid models that combine conventional simulation methodologies with quantum technology and machine learning,” explains doctoral researcher Hannah Stein.
Because researchers are using production data from real manufacturing lines, aircraft engine manufacturers could soon be using quantum computer simulations to predict blade vibrations during milling. By working with an exact digital twin, they can precisely set machining parameters, such as milling speed, allowing them to eliminate machining inaccuracies and significantly reduce rejection rates. The improved simulations also mean that laser cutters can deliver the correct amount of power during an optimized machining sequence that produces undamaged, perfectly dimensioned metal components. At this year's Hannover Fair, enterprise computing specialists from Saarbrücken will be exhibiting laser milling and cutting prototypes that demonstrate how conventional manufacturing can be improved by simulations based on quantum technology, shortening material processing times and improving product quality.
The Quasim project ("QC-Enhanced Service Ecosystem for Simulation in Manufacturing") is a consortium comprised of the research group of Wolfgang Maaß at Saarland University and the German Research Center for Artificial Intelligence (DFKI), the group of Professor Frank-Wilhelm Mauch at Forschungszentrum Jülich and Saarland University, the Fraunhofer Institute for Production Technology (IPT), the machine tool manufacturer TRUMPF, the software component provider ModuleWorks, and associated project partners Ford and the aircraft engine manufacturer MTU Aero Engines. The Federal Ministry for Economic Affairs and Climate Action (BMWK) and the German Aerospace Center (DLR) have provided total project funding of over €5 million.
