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

arXiv: https://arxiv.org/abs/2508.12440 · HTML: https://mlsanity.com/paper/2508.12440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization](https://mlsanity.com/paper/2010.14824/) (2020)
- [Component Segmentation of Engineering Drawings Using Graph Convolutional Networks](https://mlsanity.com/paper/2212.00290/) (2022)
- [An Automated Engineering Assistant: Learning Parsers for Technical Drawings](https://mlsanity.com/paper/1909.08552/) (2019)

## Abstract (arXiv)

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.