Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features
This provides a scalable solution for manufacturing industries to shorten quotation lead times and enable cost-aware design, though it builds incrementally on existing gradient-boosting methods.
The paper tackles the problem of labor-intensive manufacturing cost estimation from 2D engineering drawings by developing a machine learning framework that extracts geometric features from drawings and predicts costs with nearly 10% mean absolute percentage error.
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.