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

TL;DR

This paper introduces a hybrid graph-based machine learning approach that incorporates CAD-derived manufacturing features to improve manufacturability prediction and effort estimation for sheet metal bending.

Contribution

It enriches B-rep adjacency graphs with rule-based manufacturing features, enhancing the accuracy of process-specific manufacturability predictions.

Findings

Hybrid models outperform purely geometric approaches.

Inclusion of manufacturing features improves prediction accuracy.

Validated on both synthetic and real-world industrial datasets.

Abstract

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.