用2D图纸几何特征预测制造成本，误差仅10%。
Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features
- 从13684张工程图提取200个几何统计特征
- XGBoost等模型实现跨产品组近10%误差
- 可解释性分析揭示关键设计成本因素
我们提出一个集成机器学习框架，将2D工程图直接转化为制造成本估算。该方法从13,684张汽车悬架与转向部件的DWG图纸中，自动提取约200个几何与统计描述符，覆盖24个产品组。基于这些特征训练的梯度提升决策树模型（XGBoost、CatBoost、LightGBM）在各产品组间均达到近10%的平均绝对百分比误差，展现出超越零件特定规则的强泛化能力。结合SHAP等可解释性工具，框架识别出旋转尺寸最大值、弧线统计特征及发散度指标等几何设计驱动因素，为成本敏感设计提供可操作洞察。该端到端的CAD-to-cost流程显著缩短报价周期，确保不同零件族间成本评估的一致性与透明度，为工业4.0环境中实时、可部署的ERP集成决策支持提供可行路径。
原文摘要 · Abstract (English)
We present an integrated machine learning framework that transforms how manufacturing cost is estimated from 2D engineering drawings. Unlike traditional quotation workflows that require labor-intensive process planning, our approach about 200 geometric and statistical descriptors directly from 13,684 DWG drawings of automotive suspension and steering parts spanning 24 product groups. Gradient-boosted decision tree models (XGBoost, CatBoost, LightGBM) trained on these features achieve nearly 10% mean absolute percentage error across groups, demonstrating robust scalability beyond part-specific heuristics. By coupling cost prediction with explainability tools such as SHAP, the framework identifies geometric design drivers including rotated dimension maxima, arc statistics and divergence metrics, offering actionable insights for cost-aware design. This end-to-end CAD-to-cost pipeline shortens quotation lead times, ensures consistent and transparent cost assessments across part families and provides a deployable pathway toward real-time, ERP-integrated decision support in Industry 4.0 manufacturing environments.
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