# CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts

arXiv: https://arxiv.org/abs/2605.12266 · HTML: https://mlsanity.com/paper/2605.12266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 3 of 3)

- [Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization](https://mlsanity.com/paper/2010.14824/) (2020)
- [FoV-Net: Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Casting](https://mlsanity.com/paper/2602.24084/) (2026)
- [BenDFM: A taxonomy and synthetic CAD dataset for manufacturability assessment in sheet metal bending](https://mlsanity.com/paper/2603.13102/) (2026)

## Built on by (most cited first, 1 of 1)

- [Learn the Solid, Not the File: Canonical Inputs for Neural Networks on CAD Boundary Representations](https://mlsanity.com/paper/2609.11573/) (2026)

## Abstract (arXiv)

Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boundary Representations (B-reps), exploiting both surface geometry and topological connectivity. However, purely geometric representations often lack the process-specific semantics required for accurate manufacturability prediction: many manufacturing factors, such as surface roles or bend intent, are not explicitly encoded in shape alone and are difficult for data-driven models to infer reliably. We propose a hybrid approach that addresses this challenge by enriching B-rep attributed adjacency graphs with manufacturing features recognized through a rule-based module. Applied to sheet metal bending, recognized features, such as bend characteristics, flange lengths, and surface roles are integrated as node attributes, concentrating the learning signal on process-relevant geometric patterns. Experiments on both a large-scale synthetic manufacturability benchmark and a real-world industrial dataset with measured bending times, one of the first such validations on genuine production data, demonstrate that combining domain knowledge with graph-based learning improves prediction accuracy across both tasks. The results demonstrate that hybrid modeling offers a feasible and effective path toward deployable tools for manufacturability assessment and effort estimation in industrial CAD environments.