A Framework for Numerical Design and Optimization Algorithms

Périaux, Jacques; González, Felipe; Lee, Dong Seop Chris

doi:10.1007/978-94-017-9520-3_6

Cited by 1 publication

(1 citation statement)

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“…It originated from computational design optimization, where the optimization process considers only one discipline or subsystem. Initially, it was a structural design optimization, 5,6 which was applied to a variety of different problems: from steel frame structures 7 and aeronautics 8 to the modern field of additive manufacturing. 9 However, different disciplines were considered as well, for instance, electrical machines, 10 thermal management systems, 11 or building design.…”

Section: Introductionmentioning

confidence: 99%

Framework for metamodel-based design optimization considering product performance and assembly process complexity

Eremeev,

Cock,

Devriendt

et al. 2024

SIMULATION

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This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure.

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