arXiv (All)
open access
Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection
We use fused deposition modeling (FDM) 3D printing as a case study of how manufacturing robots can use imperfect AI. In FDM, print configuration strongly affects output quality. Yet, novice users typically rely on default configurations, trial-and-error, or direct recommendations from generic AI mod
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Creating an equation to simply approximate 3D-printed component strength by-hand dependent on planned component print orientation, print temperature, infill percentage, and surface quality for polylactic acid fused-deposition modeling printing#823246
- 3D printer systems and methods — Karl Joseph Dodds Gifford (US12202198B2)#607569
- Delta 3D printer and calibrating method for using the same — Xyzprinting, Inc. (US10046523B2)#607571
- Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning#1013944
- Contact-Implicit MPC for Multi-Orientation 3D Printing#1092513
Record · ID 1200886
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive