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October 3, 2025Open Access

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

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This framework demonstrates improved cost prediction in manufacturing through geometric feature analysis, suggesting enhanced efficiency.

The integrated machine learning framework reduces traditional manufacturing cost estimation efforts.
Models like XGBoost and CatBoost achieve nearly 10% mean absolute percentage error across various automotive parts.
The framework identifies critical geometric design drivers, facilitating efficient, cost-aware design decisions.
This CAD-to-cost pipeline supports real-time decision-making and transparency in Industry 4.0 environments.

Arikan et al. (2025) studied this question.

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October 3, 2025Open Access

View Full Paper

Ask AI

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Authors

This framework demonstrates improved cost prediction in manufacturing through geometric feature analysis, suggesting enhanced efficiency.

The integrated machine learning framework reduces traditional manufacturing cost estimation efforts.
Models like XGBoost and CatBoost achieve nearly 10% mean absolute percentage error across various automotive parts.
The framework identifies critical geometric design drivers, facilitating efficient, cost-aware design decisions.
This CAD-to-cost pipeline supports real-time decision-making and transparency in Industry 4.0 environments.

Arikan et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark