Potentials and challenges in enhancing the gear transmission development with machine learning methods—a review

• Published in: Forschung im Ingenieurwesen 2023-12, Vol.87 (4), p.1333-1346
• Main Authors: Sendlbeck, Stefan, Maurer, Matthias, Otto, Michael, Stahl, Karsten
• Format: Article
• Language: English
• Subjects: Automation; Engineering; Graph grammars; Graph neural networks; Inverse problems; Knowledge based engineering; Machine learning; Mechanical Engineering; Powertrain; Problem solving; State-of-the-art reviews; Topology; Übersichtsarbeiten/Review articles
• ISSN: 0015-7899; 1434-0860
• DOI: 10.1007/s10010-023-00699-y

The electrification of vehicle powertrains and the expected engineering labor shortage are ongoing key challenges in the gear transmission development. Because traditional methods reach limits, the solution is further automating the design process while enabling flexible and optimal design solutions even with rapidly changing constraints and requirements. We therefore review the current design process, review state-of-the-art methods for automated gear transmission design, and evaluate their potential and the challenges in combination with using machine learning methods. In focus are grammars and graph grammars in particular, which offer an approach to represent and generate the relational structure of transmission topologies or shaft arrangements. Other potential approaches are knowledge-based engineering, which allows to choose various predefined expert design solution and combine them to new designs, and constraint programming for gear transmission generation. Combining these methods with latest advances in reinforcement learning, machine learning for inverse problem-solving, and graph neural networks offers promising capabilities for automatic topology generation and dimensioning of gear transmissions.