Classifying Component Function in Product Assemblies with Graph Neural Networks
Classifying Component Function in Product Assemblies with Graph Neural Networks
复制标题
DOI：
10.1115/1.4052720
复制
发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Vincenzo Ferrero;Kaveh Hassani;Daniele Grandi;Bryony DuPont
中科院分区：
文献类型：
--
作者：
Vincenzo Ferrero;Kaveh Hassani;Daniele Grandi;Bryony DuPont
功能被定义为使产品能够完成设计目的的任务的集合。功能工具（例如功能建模）在产品设计的早期阶段（尚未做出明确的设计决策）提供决策指导。基于功能的设计数据通常很稀疏并且基于个人解释。因此，基于功能的设计工具可以受益于自动功能分类，以提高数据保真度并提供支持基于功能的智能设计代理的功能表示模型。基于功能的设计数据通常存储在手动生成的设计存储库中。这些设计存储库是产品设计中的专业知识和功能解释的集合，受功能流和组件分类法的限制。在这项工作中，我们将基于结构化分类的设计存储库表示为装配流图，然后利用图神经网络（GNN）模型来执行自动功能分类。我们通过从存储库数据中学习来建立组件功能分配的基本事实来支持自动功能分类。实验结果表明，我们的 GNN 模型在第 1 层（广泛）功能上实现了微平均 F1 分数 0.832，在第 2 层功能上实现了 0.756，在第 3 层（特定）功能上实现了 0.783 的微平均 F1 分数。考虑到数据特征的不平衡，结果令人鼓舞。我们在本文中所做的努力可以成为基于知识的 CAD 系统中更复杂的应用程序以及基于功能的设计中的 Design-for-X 考虑的起点。
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function- based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learn- ing from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems, and Design-for-X consideration in function-based design.