AIMold：一种用于复杂模具设计的自主AI驱动流水线
AIMold: An Autonomous AI-based Pipeline for Complex Mold Design
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中文总结 AI 辅助
研究针对复杂模具设计依赖专家知识、缺乏公开数据集的问题，推出含超2.3万个模型的MoldCAD数据集，提出AI流水线实现模具设计自动化，助力制造感知型CAD生成。
中文摘要 AI 辅助
注塑成型是塑料部件大规模生产的核心工艺。现有算法可利用标准两开式模具实现基础几何形状的模具设计自动化，但带有倒扣、侧孔或凹入特征的复杂零件面临重大挑战，这类几何形状通常需要主上下模具之外的辅助部件。实际中，设计这类复杂装配体是依赖专家知识的繁琐过程，且公开数据集的匮乏阻碍了有效基于学习的解决方案的开发。为填补这些空白，我们推出MoldCAD数据集，该数据集将复杂的单实体CAD零件与工业标准模具装配体配对，每个条目包含上下模具、分型面、脱模方向及必要的辅助部件，数据集包含4934个CAD模型、超过3850个模具装配体，总计超2.3万个独立模型。基于该数据集，我们提出一套综合流水线，可预测脱模方向、识别辅助部件并构建分型面，以生成适用于下游CAD/CAM工作流的完整可制造模具装配体。我们的结果展示了通往完全自动化工业模具设计的可行路径，并为制造感知型CAD生成的更广泛发展做出贡献。
英文摘要
Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designing these intricate assemblies is a laborious process that relies heavily on expert knowledge. Furthermore, the scarcity of public datasets has hindered the development of effective learning-based solutions. To bridge these gaps, we introduce MoldCAD, a curated dataset that pairs complex single-body CAD parts with industry-standard mold assemblies. Each entry includes the upper and lower molds, parting surfaces, demolding orientations, and necessary auxiliary components. The dataset comprises 4,934 CAD models and over 3,850 mold assemblies, totaling more than 23k individual models. Building upon this dataset, we propose a comprehensive pipeline that predicts demolding orientations, identifies auxiliary components, and constructs parting surfaces to derive a complete, manufacturing-ready mold assembly for downstream CAD/CAM workflows. Our results demonstrate a promising path toward fully automated industrial mold design and contribute to the broader advancement of manufacturing-aware CAD generation.
发表机构
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）
- Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）
- FNii-Shenzhen（深圳未来网络研究院）
机构由 AI 辅助整理，请以论文原文为准。
