CNCGEN: A Dataset and Framework for Machining Process Planning and Toolpath Generation from B-rep Models

2026-09-30

AI Summary

This study addresses the challenge of coupling discrete operation planning with continuous toolpath generation in CNC machining of B-rep models by proposing the CNCGEN framework. This framework introduces a novel "persistent manufacturing object" modeling mechanism that, combined with a learned agent verifier providing material removal feedback, dynamically correlates local predictions with geometric evolution to enable stepwise generation and state updating of operations and toolpaths. The approach integrates deep learning for three-axis machining, B-rep representations, and parametric toolpath algorithms, supported by a synthetically generated dataset incorporating geometric verification. Experimental results demonstrate that, compared to baseline methods, the proposed framework significantly improves workpiece geometric accuracy while effectively mitigating residual material and overcutting phenomena.

Abstract

Learning to generate machining process plans and toolpaths from B-rep CAD requires coupling discrete operation decisions with continuous tool motion as the workpiece evolves. Correctly predicting an operation sequence does not by itself ensure correct material removal, because each toolpath acts on the stock left by preceding cuts. We formulate this problem around persistent manufacturing objects: object identity determines the target of an operation, while the evolving stock state conditions the generation of its toolpath. Based on this formulation, we propose CNCGEN, a dataset and learning framework for three-axis machining. CNCGEN-Dataset contains approximately 50k geometrically verified synthetic machining flows and 800 held-out real CNC records. Each flow aligns B-rep geometry with object-referenced operations, parameterized toolpaths, intermediate stock states, and verification outcomes, enabling supervision of the correspondence between planning decisions and their geometric effects. CNCGEN generates operations and toolpaths for selected objects step by step, updating a compact machining state to guide subsequent predictions. During training, a learned surrogate verifier provides material-removal feedback that links local predictions to their geometric consequences. Experiments on synthetic and held-out real CNC records show that CNCGEN improves the resulting workpiece geometry and reduces residual material and overcut compared with adapted CNC generation baselines.

Problem

• Machining process planning
• Toolpath generation
• B-rep models
• CNC manufacturing
• Stock state

Innovation

• B-rep Models
• Toolpath Generation
• Process Planning
• Surrogate Verifier
• Machining State