Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework

2026-06-19

AI Summary

This work proposes a TRIZ-inspired text-to-CAD generation framework that addresses the limited support for creative design exploration in existing large language model (LLM)-driven approaches, which typically prioritize geometric fidelity and instruction following. The proposed method employs a three-stage generate–augment–optimize pipeline, integrating TRIZ inventive principles—such as segmentation, counterweight, and dynamization—into prompt engineering strategies to guide LLMs toward automatically producing structurally diverse and editable CAD variants under technical contradiction constraints. Coupled with a structural integrity preservation algorithm, the framework successfully generates multiple lightweight chair designs, achieving mass reductions of 4.0%–14.7% while maintaining structural soundness.

Abstract

Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided design (CAD) automation. However, most existing LLM-based 3D CAD generation approaches primarily focus on geometric precision and instruction-following performance, often overlooking the fundamental aspect of creative design exploration. This study presents a TRIZ-inspired text-to-CAD framework that leverages LLMs to generate high-quality, editable CAD models while systematically exploring creative design alternatives. The framework integrates the Theory of Inventive Problem Solving (TRIZ)-embedding deep human insights from extensive patent records-into LLM prompting strategies, enabling autonomous generation of innovative CAD variants that address technical contradictions. Through a comprehensive three-stage pipeline of design generation, enhancement, and optimization, the framework produces structurally diverse CAD models from well-crafted prompts. The present study implements and evaluates the first two stages, while positioning the design optimization stage as future work. A product design case study (chair) demonstrates that the TRIZ-inspired text-to-CAD framework generates multiple creative design alternatives by systematically applying TRIZ inventive principles such as segmentation, anti-weight, dynamics, and composite materials, achieving 4.0-14.7% mass reduction across all enhanced designs while maintaining structural integrity. The key findings suggest that integrating systematic innovation methodologies with LLM-based 3D CAD generation bridges the gap between precision-focused synthesis and creativity-focused exploration, advancing toward autonomous design systems where AI makes design decisions independently, supporting human decision-making in human-AI collaborative design for engineering applications.

Problem

• 3D generative design
• creativity
• TRIZ
• LLM-based CAD generation
• design exploration

Innovation

• TRIZ
• text-to-CAD
• large language models
• creative design
• generative design