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1. Orthogonal Distance Fields Representation for Machine-Learning Based Manufacturability Analysis

   https://doi.org/10.1115/DETC2020-22487  

   Balu, Aditya; Ghadai, Sambit; Sarkar, Soumik; Krishnamurthy, Adarsh (August 2020, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE))

   Abstract Computer-aided Design for Manufacturing (DFM) systems play an essential role in reducing the time taken for product development by providing manufacturability feedback to the designer before the manufacturing phase. Traditionally, DFM rules are hand-crafted and used to accelerate the engineering product design process by integrating manufacturability analysis during design. Recently, the feasibility of using a machine learning-based DFM tool in intelligently applying the DFM rules have been studied. These tools use a voxelized representation of the design and then use a 3D-Convolutional Neural Network (3D-CNN), to provide manufacturability feedback. Although these frameworks work effectively, there are some limitations to the voxelized representation of the design. In this paper, we introduce a new representation of the computer-aided design (CAD) model using orthogonal distance fields (ODF). We provide a GPU-accelerated algorithm to convert standard boundary representation (B-rep) CAD models into ODF representation. Using the ODF representation, we build a machine learning framework, similar to earlier approaches, to create a machine learning-based DFM system to provide manufacturability feedback. As proof of concept, we apply this framework to assess the manufacturability of drilled holes. The framework has an accuracy of more than 84% correctly classifying the manufacturable and non-manufacturable models using the new representation. 

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2. TransformCAD: Multimodal Transformer for Computer-Aided Design Generation

   https://doi.org/10.1115/DETC2025-168790  

   Sun, Yuewan; Sha, Zhenghui (August 2025, American Society of Mechanical Engineers)

   The creation of manufacturable and modifiable 3D shapes using Computer-Aided Design (CAD) remains a predominantly manual and time-consuming process, hindered by the complexity of boundary representations in 3D solids and the lack of intuitive design tools. This paper introduces TransformCAD, a CAD generation model that leverages both image and natural language descriptions as input to generate CAD sequences, producing editable 3D representations relevant to engineering design. TransformCAD incorporates a fine-tuned Contrastive Language-Image Pre-Training (CLIP) model to process multimodal input and employs two prediction branches—sketch and extrude—to enhance the parsing rate of CAD generation. Extensive evaluations demonstrate that TransformCAD outperforms existing models in terms of parsing rate, Chamfer distance, minimum matching distance, and Jensen-Shannon divergence. Furthermore, by analyzing the impact of training data, we show that TransformCAD exhibits strong potential for accurately generating long-sequence CAD models, which correspond to higher-complexity designs. Moreover, real-world 3D object images taken by a smartphone are used to validate TransformCAD’s practicability, demonstrating its effectiveness in industrial applications. To the best of our knowledge, this is the first attempt at generating 3D CAD models integrating both image and natural language input. TransformCAD expands the boundaries of automated CAD modeling, enabling a more flexible and intuitive design process that bridges visual perception and structured command-based representations. 

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3. DeepCAD: A Deep Generative Network for Computer-Aided Design Models

   https://doi.org/10.1109/ICCV48922.2021.00670  

   Wu, Rundi; Zheng, Changxi (January 2021, The International Conference on Computer Vision (ICCV))

   Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation--describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic. 

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4. DeepCAD: A Deep Generative Network for Computer-Aided Design Models

   https://doi.org/10.1109/ICCV48922.2021.00670  

   Wu, Rundi; Xiao, Chang; Zheng, Changxi (October 2021, International Conference on Computer Vision)

   Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation—describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic. 

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5. VITRUVION: A GENERATIVE MODEL OF PARAMETRIC CAD SKETCHES

   Seff, A.; Zhou, W.; Richardson, N.; Adams, R. P. (April 2022, The International Conference on Learning Representations (ICLR))

   Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the de- sign process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to down- stream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow. 

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