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.

中文翻译：

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考虑产品性能和装配工艺复杂性的基于元模型的设计优化框架

本文提出了一种同时评估装配工艺复杂性和未来产品性能的方法。它允许产品设计优化，在开发过程的早期阶段考虑未来设计的不同方面。所提出的方法体现在完全自动化的框架中，用更高效、更快速的组合程序取代了传统的顺序开发过程，同时解决了多个设计方面的问题。装配设计 (DFA) 规则用作整个产品和单个装配操作的易于装配性的定量指标，与基于有限元 (FE) 模拟估计的性能指标一起自动评估。此优化问题的直接解决方案可能效率低下或不可能，因为它需要对计算成本昂贵的离散和连续函数进行反复评估，这些函数具有代表优化目标和约束的未知行为。因此，所提出的框架采用基于高斯过程和人工神经网络的回归模型，从而通过基于元模型的设计优化（MBDO）实现产品的优化设计。考虑到齿轮箱组件的机械性能和装配工艺，所建议的方法在齿轮箱组件的优化中得到了证明。比较基于元模型和直接设计优化的结果表明，MBDO 允许使用小三倍的计算预算找到更好的解决方案。此外，对使用不同大小的固定采样数据集获得的结果的分析突出了所采用的采样程序的局限性。