Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features

TL;DR

This paper introduces a machine learning framework that predicts manufacturing costs from 2D engineering drawings using geometric features, enabling faster, more transparent, and scalable cost estimation in automotive manufacturing.

Contribution

The study develops an end-to-end CAD-to-cost pipeline utilizing geometric descriptors and gradient-boosted models, improving accuracy and interpretability over traditional methods.

Findings

Achieved nearly 10% mean absolute percentage error in cost prediction.

Identified key geometric features influencing manufacturing costs.

Demonstrated scalability across multiple automotive part groups.

Abstract

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.