Deep learning-based inverse design for engineering systems: multidisciplinary design optimization of automotive brakes

The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most representative factors for evaluating braking performance. In particular, as the two performance factors have a conflicting r...

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Veröffentlicht in:Structural and multidisciplinary optimization Jg. 65; H. 11
Hauptverfasser: Kim, Seongsin, Jwa, Minyoung, Lee, Soonwook, Park, Sunghoon, Kang, Namwoo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022
Springer Nature B.V
Schlagworte:
Back propagation
Brakes
Braking
Computational efficiency
Computational Mathematics and Numerical Analysis
Computing costs
Deep learning
Design optimization
Drag
Engineering
Engineering Design
Inverse design
Iterative methods
Multidisciplinary design optimization
Performance evaluation
Quadratic programming
Research Paper
Theoretical and Applied Mechanics
Torque
Deep learning
Brake system
Inverse design
Multidisciplinary design optimization
ISSN:1615-147X, 1615-1488
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Zusammenfassung:The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most representative factors for evaluating braking performance. In particular, as the two performance factors have a conflicting relationship with each other, a multidisciplinary design optimization (MDO) approach is required for brake design. However, the computational cost of MDO increases as the number of disciplines increases. Recent studies on inverse design that use deep learning (DL) have established the possibility of instantly generating an optimal design that can satisfy the target performance without implementing an iterative optimization process. This study proposes a DL-based multidisciplinary inverse design (MID) that simultaneously satisfies multiple targets, such as the APT and drag torque of the brake system. Results show that the proposed inverse design can find the optimal design more efficiently compared with the conventional optimization methods, such as backpropagation and sequential quadratic programming. The MID achieved a similar performance to the single-disciplinary inverse design in terms of accuracy and computational cost. A novel design was derived on the basis of results, and the same performance was satisfied as that of the existing design.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03386-8