基于几何与知识基础的大语言模型推理的任务驱动型3D可打印性辅助
Task-Driven 3D Printability Assistance via Geometry- and Knowledge-Grounded LLM Reasoning
- Colorado School of Mines（科罗拉多矿业学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出一种基于几何与知识基础的LLM推理的任务驱动型3D可打印性辅助框架，可生成多维度结构化打印建议，经实验验证其可提升可打印性、任务适用性及材料选择准确率。
AI中文摘要：
增材制造中的可打印性评估通常在打印前于几何层面进行，以确定计算机辅助设计（CAD）模型或立体光刻（STL）文件能否成功制造；而任务适用性则通常在打印后评估，以确定制造的部件是否满足预期用途要求。因此，非专业用户打印功能部件时，不适用的材料或工艺选择可能仅在制造后才被发现，导致重复打印、材料浪费和用户挫败感。为解决这一挑战，本文利用大语言模型（LLM）的推理与语言理解能力，同时以几何证据及结构化材料/打印机知识为推理基础，生成可靠的打印前建议。给定立体光刻（STL）模型和自然语言任务描述，该框架生成涵盖可打印性、材料选择、工艺参数、设计指导、风险及解释的结构化建议。我们在带有新手式任务描述的聚焦STL基准场景中评估该框架：在96次物理验证试验中，所提方法实现了75.0%的可打印性，成功打印样本中任务适用性达88.9%；还将Gemini 2.5 Flash-Lite的材料选择准确率从纯LLM下的37.5%提升至90.0%。专家评估显示报告质量有所提升，打印后反馈也改进了部分问题案例的建议。这些结果表明，用户任务意图、几何证据和结构化材料知识对可靠的任务驱动型可打印性辅助均至关重要。
英文摘要：
Printability assessment in additive manufacturing is typically conducted at the geometry level before printing to determine whether a computer-aided design (CAD) model or stereolithography (STL) file can be successfully fabricated. Task suitability, in contrast, is usually evaluated after printing to determine whether the fabricated part satisfies the requirements of its intended use. As a result, for non-expert users to print functional parts, unsuitable material or process choices may only be identified after fabrication, leading to repeated printing, material waste, and user frustration. To address this challenge, this paper leverages the reasoning and language-understanding capabilities of large language models (LLMs), while grounding the reasoning with geometry evidence and structured material/printer knowledge to generate reliable pre-print recommendations. Given a stereolithography (STL) model and a natural-language task description, the framework generates a structured recommendation covering printability, material choice, process parameters, design guidance, risks, and explanations. We evaluate the framework on focused STL benchmark scenarios with novice-style task descriptions. The proposed method achieves 75.0% printability over 96 physical validation trials, with 88.9% task suitability among successfully printed samples. It also improves Gemini 2.5 Flash-Lite material-selection accuracy from 37.5% under pure LLM to 90.0%. Expert evaluation further shows improved report quality, while post-print feedback improves recommendations on selected problematic cases. These results suggest that user task intent, geometry evidence, and structured material knowledge are all important for reliable task-driven printability assistance.
