AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition

2026-07-02

AI Summary

This work addresses the persistent challenges in fused deposition modeling (FDM) printing—such as poor printability, insufficient mechanical strength, and complex post-processing caused by geometric defects like steep overhangs—by introducing the first end-to-end multi-agent system capable of automatically repairing original CAD models. The proposed framework integrates B-Rep parsing, graph neural network–based semantic recognition, and multimodal large language model reasoning to detect manufacturability issues and generate optimized STEP files along with detailed modification reports. By constructing face adjacency topological graphs, applying GraphSAGE for semantic labeling, leveraging Claude Sonnet for design suggestions, and validating modifications via GPT-4o’s visual reasoning, the system automates the entire pipeline from geometric analysis to natural-language design recommendations. Evaluated on a birdhouse model, it accurately identified overhang regions and effectively proposed corrective strategies such as chamfering, filleting, or part reorientation, substantially overcoming the reliance on manual intervention inherent in traditional design-for-manufacturing approaches.

Abstract

Parts manufactured with Fused Deposition Modeling (FDM) often require Design for Additive Manufacturing (DFAM) modifications to ensure printability, structural integrity, and reduced post-processing. Current slicers identify defects such as steep overhangs but are unable to modify the underlying geometry. This work presents AgentsCAD, a multi-agent system that bridges raw boundary-representation (B-Rep) geometry and Large Language Model (LLM) reasoning to automate targeted DFM. The workflow begins by parsing a STEP file. The agentic system detects overhangs above a 45°threshold, constructs a face-adjacency topology graph, and optionally injects semantic feature labels from a GraphSAGE model trained on MFCAD++ (59,665 parts), before dispatching a Claude Sonnet design-reasoning agent that recommends reorientations, fillets, chamfers, and similar modifications. A GPT-4o vision-language verifier inspects rendered views to confirm geometric integrity. Outputs include a modified STEP file and a human-readable report. A test case on a birdhouse model demonstrates that the system correctly diagnoses overhangs, selects appropriate defect mitigation strategies, and proposes physically valid corrections, partially solving the geometry-to-language translation problem central to LLM-driven CAD modification.

Problem

• Design for Additive Manufacturing
• Fused Deposition Modeling
• geometric modification
• overhang detection
• CAD automation

Innovation

• multi-agent LLM
• geometric feature recognition
• Design for Additive Manufacturing (DFAM)
• B-Rep reasoning
• GraphSAGE