Knowledge graph construction for product designs from large CAD model repositories |
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Affiliation: | 1. School of Computer Science and Technology, Hangzhou Dianzi University, China;2. State Key Laboratory of CAD&CG, Zhejiang University, China;3. School of Computer Science and Engineering, Beifang University of Nationalities, China;1. Università degli Studi di Parma, Parco area delle scienze 181/A, 43121 Parma, Italy;2. Università Politecnica delle Marche, via brecce bianche n. 12, 60121 Ancona, Italy;1. Yangzhou Polytechnic Institute, China;2. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;3. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta T2N 1N4, Canada;4. NingboTech University, Ningbo 315100, China;1. School of Management, Jiangsu University, 212013 Zhenjiang, PR China;2. School of Economics and Management, Beihang University, 100191 Beijing, PR China;1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;2. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310000, China |
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Abstract: | Product Design based Knowledge graphs (KG) aid the representation of product assemblies through heterogeneous relationships that link entities obtained from multiple structured and unstructured sources. This study describes an approach to constructing a multi-relational and multi-hierarchical knowledge graph that extracts information contained within the 3D product model data to construct Assembly-Subassembly-Part and Shape Similarity relationships. This approach builds on a combination of utilizing 3D model meta-data and structuring the graph using the Assembly-Part hierarchy alongside 3D Shape-based Clustering. To demonstrate our approach, from a dataset consisting of 110,770 CAD models, 92,715 models were organized into 7,651 groups of varying sizes containing highly similar shapes, demonstrating the varied nature of design repositories, but inevitably also containing a significant number of repetitive and unique designs. Using the Product Design Knowledge Graph, we demonstrate the effectiveness of 3D shape retrieval using Approximate Nearest Neighbor search. Finally, we illustrate the use of the KG for Design Reuse of co-occurring components, Rule-Based Inference for Assembly Similarity and Collaborative Filtering for Multi-Modal Search of manufacturing process conditions. Future work aims to expand the KG to include downstream data within product manufacturing and towards improved reasoning methods to provide actionable suggestions for design bot assistants and manufacturing automation. |
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Keywords: | Digital manufacturing 3D shape search 3D recommendation Deep learning |
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