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1. Studying Changes to the Additive Manufacturability of Design Solutions When Prepared and Simulated in Immersive Virtual Reality

   https://doi.org/10.1115/DETC2024-143204  

   Mathur, Jayant; Miller, Scarlett R; Simpson, Timothy W; Meisel, Nicholas A (August 2024, American Society of Mechanical Engineers)

   Abstract Solving problems with additive manufacturing (AM) often means fabricating geometrically complex designs, layer-by-layer, along one or multiple directions. Designers navigate this 3D spatial complexity to determine the best design and manufacturing solutions to produce functional parts, manufacturable by AM. However, to assess the manufacturability of their solutions, designers need modalities that naturally visualize AM processes and the designs enabled by them. Creating physical parts offers such visualization but becomes expensive and time-consuming over multiple design iterations. While non-immersive simulations can alleviate this cost of physical visualization, adding digital immersion further improves outcomes from the visualization experience. This research, therefore, studies how differences in immersion between computer-aided (CAx) and virtual reality (VR) environments affect: 1. determining the best solution for additively manufacturing a design and 2. the cognitive load experienced from completing the DfAM problem-solving experience. For the study, designers created a 3D manifold model and simulated manufacturing it in either CAx or VR. Analysis of the filtered data from the study shows that slicing and printing their designs in VR yields a significant change in the manufacturability outcomes of their design compared to CAx. No observable differences were found in the cognitive load experienced between the two modalities. This means that the experiences in VR may influence improvements to manufacturability outcomes without changes to the mental exertion experienced by the designers. This presents key implications for how designers are equipped to solve design problems with AM. 

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2. Image2CADSeq: Computer-Aided Design Sequence and Knowledge Inference From Product Images

   https://doi.org/10.1115/DETC2025-168988  

   Li, Xingang; Sha, Zhenghui (August 2025, American Society of Mechanical Engineers)

   Computer-aided design (CAD) tools empower designers to design and modify 3D models through a series of CAD operations, commonly referred to as a CAD sequence. In scenarios where digital CAD files are inaccessible, reverse engineering (RE) has been used to reconstruct 3D CAD models. Recent advances have seen the rise of data-driven approaches for RE, with a primary focus on converting 3D data, such as point clouds, into 3D models in boundary representation (B-rep) format. However, obtaining 3D data poses significant challenges, and B-rep models do not reveal knowledge about the 3D modeling process of designs. To this end, our research introduces a novel data-driven approach based on representation learning to infer CAD sequences from product images, coined as Image2CADSeq. These sequences can then be translated into B-rep models using a solid modeling kernel. Unlike B-rep models, CAD sequences offer enhanced flexibility to modify individual steps of model creation, providing a deeper understanding of the construction process of CAD models. One unique contribution of this paper is the development of a multi-level evaluation framework for model assessment, so the predictive performance of the Image2CADSeq model can be rigorously evaluated. The model was trained on a specially synthesized dataset, and various neural network architectures were explored to optimize the performance. The experimental and validation results show the great potential of our model in data-driven reverse engineering of 3D CAD models from 2D images. 

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3. Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

   Ghadai, Sambit; Balu, Aditya; Krishnamurthy, Adarsh; Sarkar, Soumik (January 2017, NIPS Symposium on Interpretable Machine Learning)

   3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D-GradCAM) for visual explanations of the distinct local geometric features of interest within an object. To enable efficient learning of 3D geometries, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D GradCAM. An application of this feature identification framework is to recognize difficult-to-manufacture drilled hole features in a complex CAD geometry. The framework can be extended to identify difficult-to-manufacture features at multiple spatial scales leading to a real-time design for manufacturability decision support system. 

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4. Peak your Frequency: Advanced Search of 3D CAD Files in the Fourier Domain

   https://doi.org/10.1109/ACCESS.2020.3013284  

   Mouris, Dimitris; Gouert, Charles; Gupta, Nikhil; Tsoutsos, Nektarios Georgios (July 2020, IEEE Access)

   An ever-increasing number of industries are adopting additive manufacturing (AM), also known as 3D printing, to their production lifecycles for manufacturing parts. A computer aided design (CAD) model is used to manufacture the part. The capability for efficient search and retrieval of the CAD models from the database has become an essential need for designers and users. However, traditional search techniques perform poorly in the context of searching CAD designs. In this paper, we propose Fourier Fingerprint Search (FFS), a retrieval framework for 3D models that deduces and leverages critical shape characteristics for search. FFS introduces a novel search methodology that incorporates these characteristics and uses two advanced matching techniques that operate at different granularities and take into account unique patterns associated with each design. In addition, FFS supports both exact and partial matching in order to provide helpful and robust search results for any scenario. We investigate a diverse set of features and enhancements for search that allows for high adaptability in all situations, such as dividing shapes into smaller parts, surface interpolation, and two different types of rotation. We evaluate FFS using the FabWave CAD dataset with approximately 3000 manufacturing models with different configurations. Our experimental results demonstrate the efficiency and high accuracy of our approach for both exact and partial matching, rendering FFS a powerful framework for CAD model search. 

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5. 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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