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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.19490 |
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| _version_ | 1866913859407708160 |
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| author | Liao, Jianxing Xu, Junyan Sun, Yatao Tang, Maowen He, Sicheng Liao, Jingxian Yu, Shui Li, Yun Xiao, Hongguan |
| author_facet | Liao, Jianxing Xu, Junyan Sun, Yatao Tang, Maowen He, Sicheng Liao, Jingxian Yu, Shui Li, Yun Xiao, Hongguan |
| contents | Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models Liao, Jianxing Xu, Junyan Sun, Yatao Tang, Maowen He, Sicheng Liao, Jingxian Yu, Shui Li, Yun Xiao, Hongguan Artificial Intelligence I.2.7; I.2.6 Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/ |
| title | Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models |
| topic | Artificial Intelligence I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2505.19490 |