Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing (QUASIM)

With more than 390,000 companies and around 3.7 million employees, the metalworking industry represents the largest secondary sector within the EU-28 (cf. Eurostat, Sectoral analysis of key indicators). In this sector, machining represents one of the most important manufacturing technologies.

Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing (QUASIM)

Numerous key German industries generate a large part of their value added on the product by means of machining, such as tool and die making, the semiconductor industry or engine construction. Due to the high importance of machining, the companies concerned are interested in the continuous optimization of machining processes in terms of quality, productivity, economic efficiency and, increasingly, sustainability.

Through digitization, machining processes are represented by digital twins, enabling end-to-end planning, manufacturing and quality assurance.  In industrial applications, models and simulations based on digital twins are mostly excluded due to their computational requirements and the expert knowledge needed to operate them. As a consequence, relevant physical effects in industrial practice are either neglected or only approximated by rough estimations. As a result, the quality of the digital twin and the knowledge and decisions derived from it suffer considerably, which in many cases leads to significant economic disadvantages in industry.

Due to the high quality requirements and the usually considerable costs for rejects, simulations based on digital twins enable optimized machining processes to be planned. These technology-specific simulation models mainly come from the three categories analytics (e.g. Euler-Bernoulli bending beam model), numerics (e.g. Dexel-based meshing simulation) and increasingly also the field of machine learning (ML) (e.g. neural networks). Especially the models from the categories numerics and ML regularly lead even powerful digital infrastructures to their limits, since they are still based on conventional semiconductor computers and their technical functionality. The resulting lengthy computation times and/or erroneous computation results still hamper the transfer of complete Industrie 4.0 framework models to industry today. Initial studies show that quantum mechanical functional principles have decisive advantages in solving numerous algorithmic problems, such as significant accelerations in numerical procedures and improvements in results through "quantum machine learning"-based approaches.

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Last Modified: 14.05.2023