A method for detecting process design intent in the process route based on heterogeneous graph convolutional networks

• Published in: Robotics and computer-integrated manufacturing 2025-04, Vol.92, p.102872, Article 102872
• Main Authors: Liang, Jiachen, Zhang, Shusheng, Xu, Changhong, Zhang, Yajun, Huang, Rui, Zhang, Hang, Wang, Zhen
• Format: Article
• Language: English
• Subjects: Design cognition; Heterogeneous graph convolutional neural networks; Machining process route; Process design intent
• ISSN: 0736-5845
• DOI: 10.1016/j.rcim.2024.102872

•We propose and define the concept of process design intent to represent the implicit design thinking and experience of the technologist contained in the process route during the process design.•We propose a prediction method for process design intent in process routes based on heterogeneous graph convolutional networks. This method represents the 3D model and its associated process routes as heterogeneous graphs as inputs to the network. Through network training, it is possible to accurately predict the key elements that cause process design intents in parts, as well as the specific process design intents present in the process route.•Our work interprets the process design from the perspective of design cognition and reveals the rationales in the generated process results. It shows the interpretability and guidance of the process design intent during the process generation. This enables technologists not only to know how to do but also to deeply understand the “knowing why” behind specific skills. The process design intent is the concentration of the technologists’ design cognitive process which contains the experiential knowledge and skills. It can reproduce technologists’ design thinking process in process design and provides guidance and interpretability for the generation of process results. The machining process route, as a core component of a part's entire manufacturing process, contains substantial process design intent. If the process design intent embedded in the existing process route can be explicitly identified, subsequent technologists will be able to learn and understand the original designers’ thinking, methodologies, and intents. This understanding enables effective reuse of design thinking and logic in the process design of new parts, rather than merely reusing data. It can also promote the propagation of the expertise and skills inherent in the process design intent. However, existing research on process design intent lacks a detailed explanation of its formation and specific structure from the design cognition perspective, making it challenging to effectively predict the process design intent containing interpretable empirical knowledge in the process route. To address this issue, this paper provides a method for predicting process design intent in the process route using heterogeneous graph convolutional networks. First, the heterogeneous graph is used to represent the parts and their associated process routes in the dataset. The nodes