@article{bharadwaj_starly_2022, title={Knowledge graph construction for product designs from large CAD model repositories}, volume={53}, ISSN={["1873-5320"]}, DOI={10.1016/j.aei.2022.101680}, abstractNote={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.}, journal={ADVANCED ENGINEERING INFORMATICS}, author={Bharadwaj, Akshay G. and Starly, Binil}, year={2022}, month={Aug} }
 @article{angrish_bharadwaj_starly_2021, title={MVCNN plus plus : Computer-Aided Design Model Shape Classification and Retrieval Using Multi-View Convolutional Neural Networks}, volume={21}, ISBN={1944-7078}, DOI={10.1115/1.4047486}, abstractNote={Abstract
               Deep neural networks (DNNs) have been successful in classification and retrieval tasks of images and text, as well as in the graphics domain. However, these DNNs algorithms do not translate to 3D engineering models used in the product design and manufacturing. This paper studies the use of multi-view convolutional neural network (MVCNN) algorithm enhanced by the addition of engineering metadata, for classification and retrieval of 3D computer-aided design (CAD) models. The proposed algorithm (MVCNN++) builds on the MVCNN algorithm with the addition of part dimension data, improving its efficacy for manufacturing part classification and yielding an improvement in classification accuracy of 5.8% over the original version. Unlike datasets used for 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D CAD models do not yield themselves to neat, distinct classes. Techniques such as relaxed-classification and prime angled cameras for capturing feature detail were used to address training data capture issues specific to 3D CAD models, along with the use of transfer learning to reduce training time. Our study has shown that DNNs can be used to search and discover relevant 3D engineering models in large public repositories, making 3D models accessible to the community.}, number={1}, journal={JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING}, author={Angrish, Atin and Bharadwaj, Akshay and Starly, Binil}, year={2021} }