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1. LLM4CAD: Multi-Modal Large Language Models for 3D Computer-Aided Design Generation

   https://doi.org/10.1115/DETC2024-143740  

   Li, Xingang; Sun, Yuewan; Sha, Zhenghui (August 2024, American Society of Mechanical Engineers)

   The evolution of multimodal large language models (LLMs) capable of processing diverse input modalities (e.g., text and images) holds new prospects for their application in engineering design, such as the generation of 3D computer-aided design (CAD) models. However, little is known about the ability of multimodal LLMs to generate 3D design objects, and there is a lack of quantitative assessment. In this study, we develop an approach to enable two LLMs, GPT-4 and GPT-4V, to generate 3D CAD models (i.e., LLM4CAD) and perform experiments to evaluate their efficacy. To address the challenge of data scarcity for multimodal LLM studies, we created a data synthesis pipeline to generate CAD models, sketches, and image data of typical mechanical components (e.g., gears and springs) and collect their natural-language descriptions with dimensional information using Amazon Mechanical Turk. We positioned the CAD program (programming script for CAD design) as a bridge, facilitating the conversion of LLMs’ textual output into tangible CAD design objects. We focus on two critical capabilities: the generation of syntactically correct CAD programs (Cap1) and the accuracy of the parsed 3D shapes (Cap2) quantified by intersection over union. The results show that both GPT-4 and GPT-4V demonstrate potential in 3D CAD generation. Specifically, on average, GPT-4V outperforms when processing only text-based input, exceeding the results obtained using multimodal inputs, such as text with image, for Cap 1 and Cap 2. However, when examining category-specific results of mechanical components, while the same trend still holds for Cap 2, the prominence of multimodal inputs is increasingly evident for more complex geometries (e.g., springs and gears) in Cap 1. The potential of multimodal LLMs in enhancing 3D CAD generation is clear, but their application must be carefully calibrated to the complexity of the target CAD models to be generated. 

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2. Large Language Models for Computer-Aided Design Fine Tuned: Dataset and Experiments

   https://doi.org/10.1115/1.4067713  

   Sun, Yuewan; Li, Xingang; Sha, Zhenghui (April 2025, Journal of Mechanical Design)

   Despite the power of large language models (LLMs) in various cross-modal generation tasks, their ability to generate 3D computer-aided design (CAD) models from text remains underexplored due to the scarcity of suitable datasets. Additionally, there is a lack of multimodal CAD datasets that include both reconstruction parameters and text descriptions, which are essential for the quantitative evaluation of the CAD generation capabilities of multimodal LLMs. To address these challenges, we developed a dataset of CAD models, sketches, and image data for representative mechanical components such as gears, shafts, and springs, along with natural language descriptions collected via Amazon Mechanical Turk. By using CAD programs as a bridge, we facilitate the conversion of textual output from LLMs into precise 3D CAD designs. To enhance the text-to-CAD generation capabilities of GPT models and demonstrate the utility of our dataset, we developed a pipeline to generate fine-tuning training data for GPT-3.5. We fine-tuned four GPT-3.5 models with various data sampling strategies based on the length of a CAD program. We evaluated these models using parsing rate and intersection over union (IoU) metrics, comparing their performance to that of GPT-4 without fine-tuning. The new knowledge gained from the comparative study on the four different fine-tuned models provided us with guidance on the selection of sampling strategies to build training datasets in fine-tuning practices of LLMs for text-to-CAD generation, considering the trade-off between part complexity, model performance, and cost. 

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3. From concept to manufacturing: evaluating vision-language models for engineering design

   https://doi.org/10.1007/s10462-025-11290-y  

   Picard, Cyril; Edwards, Kristen M; Doris, Anna C; Man, Brandon; Giannone, Giorgio; Alam, Md Ferdous; Ahmed, Faez (September 2025, Artificial Intelligence Review)

   Abstract Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this shift. Yet, with text as their only input modality, they cannot leverage the large body of visual artifacts that engineers have used for centuries and are accustomed to. This gap is addressed with the release of multimodal vision-language models (VLMs), such as GPT-4V, enabling AI to impact many more types of tasks. Our work presents a comprehensive evaluation of VLMs across a spectrum of engineering design tasks, categorized into four main areas: Conceptual Design, System-Level and Detailed Design, Manufacturing and Inspection, and Engineering Education Tasks. Specifically in this paper, we assess the capabilities of two VLMs, GPT-4V and LLaVA 1.6 34B, in design tasks such as sketch similarity analysis, CAD generation, topology optimization, manufacturability assessment, and engineering textbook problems. Through this structured evaluation, we not only explore VLMs’ proficiency in handling complex design challenges but also identify their limitations in complex engineering design applications. Our research establishes a foundation for future assessments of vision language models. It also contributes a set of benchmark testing datasets, with more than 1000 queries, for ongoing advancements and applications in this field. 

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4. 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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5. LEVIOSA: Natural Language-Based Uncrewed Aerial Vehicle Trajectory Generation

   https://doi.org/10.3390/electronics13224508  

   Aikins, Godwyll; Dao, Mawaba Pascal; Moukpe, Koboyo Josias; Eskridge, Thomas C; Nguyen, Kim-Doang (November 2024, Electronics)

   This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination. 

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